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Calculated Shard: 79 (from laksa081)

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curl -X POST \
  'http://laksa079.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.simplilearn.com/exponential-smoothing-for-time-series-forecasting-in-python-article\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.simplilearn.com/exponential-smoothing-for-time-series-forecasting-in-python-article\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.simplilearn.com\/exponential-smoothing-for-time-series-forecasting-in-python-article","crawl_time":1769398918,"first_indexed_time":1664804074,"http_code":200,"src_unparsed":"com,simplilearn!www,\/exponential-smoothing-for-time-series-forecasting-in-python-article s443","src_root_hash":"17406570482395038879","history_drop_reason":null,"meta_title":"An Introduction to Exponential Smoothing for Time Series Forecasting in Python | Simplilearn","meta_descriptions":["In this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method."],"attrs_boilerpipe_text":"Exponential smoothing is a widely preferred statistical technique to forecast a time series. We use this simple yet powerful forecasting method for smoothing univariate time series data by using the exponential window function. \nWhat is Exponential Smoothing?\nExponential smoothing is a time series method for forecasting univariate time series data.\nTime series\nmethods work on the principle that a\nprediction\nis a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. It is called so because the weight assigned to each demand observation is exponentially decreased. \nThe model assumes that the future will be somewhat the same as the recent past. The only pattern that Exponential Smoothing learns from demand history is its level - the average value around which the demand varies over time.     \nExponential smoothing is generally used to make forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. \nExponential Smoothing Forecasting\nExponential smoothing is a broadly accurate forecasting method for short-term forecasts. The technique assigns larger weights to more recent observations while assigning exponentially decreasing weights as the observations get increasingly distant. This method produces slightly unreliable long-term forecasts.\nExponential smoothing can be most effective when the time series parameters vary slowly over time. \nTypes of Exponential Smoothing\nThe main types of Exponential Smoothing forecasting methods are:\n1. Simple or Single Exponential Smoothing\nSimple or single exponential smoothing (SES) is the method of time series forecasting used with univariate data with no trend and no seasonal pattern. It needs a single parameter called alpha (a), also known as the smoothing factor. Alpha controls the rate at which the influence of past observations decreases exponentially. The parameter is often set to a value between 0 and 1.   \nThe simple exponential smoothing formula is given by:\nst = αxt+(1 – α)st-1= st-1+ α(xt – st-1)\nhere, \nst = smoothed statistic (simple weighted average of current observation xt)\nst-1 = previous smoothed statistic\nα = smoothing factor of data; 0 < α < 1\nt = time period\n2. Double Exponential Smoothing\nThis method is known as Holt's trend model or second-order exponential smoothing. Double exponential smoothing is used in time-series forecasting when the data has a linear trend but no seasonal pattern. The basic idea here is to introduce a term that can consider the possibility of the series exhibiting some trend. \nIn addition to the alpha parameter, Double exponential smoothing needs another smoothing \nfactor called beta (b), which controls the decay of the influence of change in trend. The method supports trends that change in additive ways (smoothing with linear trend) and trends that change in multiplicative ways (smoothing with exponential trend).  \nThe Double exponential smoothing formulas are:\nS1 = x1\nB1 = x1-x0\nFor t>1,\nst = αxt + (1 – α)(st-1 + bt-1)\nβt = β(st – st-1) + (1 – β)bt-1\nhere,\nbt = best estimate of the trend at time t\nβ = trend smoothing factor; 0 < β <1\n3. Triple Exponential Smoothing\nThis method is the variation of exponential smoothing that's most advanced and is used for time series forecasting when the data has linear trends and seasonal patterns. The technique applies exponential smoothing three times – level smoothing, trend smoothing, and seasonal smoothing. A new smoothing parameter called gamma (g) is added to control the influence of the seasonal component.  \nThe triple exponential smoothing method is called Holt-Winters Exponential Smoothing, named after its contributors, Charles Holt and Peter Winters.\nHolt-Winters Exponential Smoothing has two categories depending on the nature of the seasonal component:\nHolt-Winter's Additive Method − for seasonality that is addictive.\nHolt-Winter's Multiplicative Method – for seasonality that is multiplicative. \nHow to Configure Exponential Smoothing\nTo configure Exponential Smoothing, analysts need to specify all the model hyperparameters explicitly. However, this can be challenging for both beginners and experts. \nInstead, numerical optimization is commonly used to search for and fund the smoothing factors (alpha, beta, gamma, phi) for the model resulting in the most negligible error. \nAn exponential smoothing method can obtain values for unknown parameters by estimating them from the observed data. The initial values and unknown parameters can be estimated by minimizing the sum of the squared errors (SSE). \nThe parameters that indicate the kind of change in trend or seasonality (for example, whether they are additive or multiplicative or whether they should be dampened) need to be specified explicitly. \nExponential Smoothing in Python\nLet us look at how to implement exponential smoothing in\nPython. \nThe Statsmodels Python library provides the implementations of Exponential Smoothing in Python. \nSingle Exponential Smoothing \nThe SimpleExpSmoothing Statsmodels class enables implementation of Single Exponential Smoothing or simple smoothing in Python. \nFirst, an instance of SimpleExpSmoothing is instantiated and passed the training data. Next, the fit() function is called, giving the fit configuration, especially the alpha value. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast. \nDouble and Triple Exponential Smoothing\nThe SimpleExpSmoothing Statsmodels class also enables the implementation of Double and Triple Exponential Smoothing in Python. \nFirst, an instance of SimpleExpSmoothing is instantiated, specifying training data and model configuration. We must define the configuration parameters for trend, damped, seasonal, and seasonal_periods. The fit() function is then called to fit the model on the training data. \nThe fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast.\nChoose the Right Program\nLooking to build a career in the exciting field of data science? Our data science courses are designed to provide you with the skills and knowledge you need to excel in this rapidly growing industry. Our expert instructors will guide you through hands-on projects, real-world scenarios, and case studies, giving you the practical experience you need to succeed. With our courses, you'll learn to analyze data, create insightful reports, and make data-driven decisions that can help drive business success.\nConclusion\nIn this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method. To know more about time series forecasting, or to start a career in data science, consider our top-ranked\nProfessional Certificate in Data Science and Generative AI\ncertification.\nUpskill yourself with our trending\nData Science courses\nand certifications\nData Science Course\nData Science Certificate Program\nApplied Data Science with Python\nFAQs\n1. What are the three types of exponential smoothing?\nThe three types of exponential smoothing are: \nSimple or single exponential smoothing\nDouble exponential smoothing\nTriple exponential smoothing\n2. What is meant by exponential smoothing?\nExponential smoothing is a method for forecasting univariate time series data. It is based on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. The technique is so called because the weight assigned to each demand observation exponentially decreases. \n3. Why is exponential smoothing best?\nExponential smoothing gives accurate and reliable forecasts to predict the next period. Analysts can analyze the projected and actual demand shown in the estimates for effective demand planning. This helps maintain accurate inventory levels. \nAdditionally, they can adjust exponential smoothing parameter values to change how quickly prior observations lose importance in calculations. This enables tweaking the relative significance of present observations to previous observations to meet the requirements of the subject area. \n4. How do you calculate exponential smoothing?\nThe simplest formula to calculate exponential smoothing is:\nst = αxt+(1 – α)st-1= st-1+ α(xt – st-1)\nhere, \nst = smoothed statistic (simple weighted average of current observation xt)\nst-1 = previous smoothed statistic\nα = smoothing factor of data; 0 < α < 1\nt = time period\n5. What is the difference between moving average and exponential smoothing?\nMoving Average and Exponential Smoothing are two important techniques used for time series forecasting. \nMoving Average is applied to data to filter random noise from it, while Exponential Smoothing applies exponential window function to data. \nMethods under the moving average smoothing process are focused on the values with their timings, while methods under exponential smoothing provide support against trend and seasonality components of time series. The exponential moving average is focused on current values.  \nWe weigh past observations equally in Moving Average, while Exponential Smoothing assigns exponentially decreasing weights to observations as they age. To put it simply, recent observations are given more weightage in forecasting compared to older observations.   \n6. How is exponential smoothing used in forecasting?\nExponential smoothing is a widely preferred forecasting method for smoothing univariate time series data using the exponential window function. The method works by assigning exponentially decreasing weights for past observations. Larger weights are assigned to more recent observations, while exponentially decreasing weights are assigned as the observations get more and more distant. \nExponential smoothing assumes that the future will be somewhat the same as the recent past and, therefore, provides forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. We can use it most effectively to make short-term forecasts when the time series parameters vary slowly over time. ","attrs_markdown":"[![Simplilearn - Online Certification Training Course Provider](https:\/\/www.simplilearn.com\/ice9\/new_logo.svgz)](https:\/\/www.simplilearn.com\/ \"Simplilearn - Online Certification Training Course Provider\")\n\nAll Courses\n\nAll Courses\n\n- [For Business](https:\/\/www.simplilearn.com\/corporate-training \"Simplilearn for Business\")\n  - [Resources](https:\/\/www.simplilearn.com\/exponential-smoothing-for-time-series-forecasting-in-python-article)\n    - [Free Courses](https:\/\/www.simplilearn.com\/skillup-free-online-courses \"Free Courses\")\n    - [Free Resources](https:\/\/www.simplilearn.com\/resources \"Free Resources\")\n- [More](https:\/\/www.simplilearn.com\/exponential-smoothing-for-time-series-forecasting-in-python-article)\n  - [Reviews](https:\/\/www.simplilearn.com\/reviews \"Reviews\")\n  - [Become a Partner](https:\/\/www.simplilearn.com\/reseller-partner-program-for-training-courses \"Partners\")\n  - [Become an Instructor](https:\/\/www.simplilearn.com\/become-our-trainer \"Become an Instructor\")\n\n[Login](https:\/\/lms.simplilearn.com\/ \"Login\")\n\nData Science & Business Analytics\n\n[Data Science & Business Analytics](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics)[AI & Machine Learning](https:\/\/www.simplilearn.com\/resources\/artificial-intelligence-machine-learning)[Project Management](https:\/\/www.simplilearn.com\/resources\/project-management)[Cyber Security](https:\/\/www.simplilearn.com\/resources\/cyber-security)[Cloud Computing](https:\/\/www.simplilearn.com\/resources\/cloud-computing)[DevOps](https:\/\/www.simplilearn.com\/resources\/devops)[Business and Leadership](https:\/\/www.simplilearn.com\/resources\/business-and-leadership)[Quality Management](https:\/\/www.simplilearn.com\/resources\/quality-management)[Software Development](https:\/\/www.simplilearn.com\/resources\/software-development)[Agile and Scrum](https:\/\/www.simplilearn.com\/resources\/agile-and-scrum)[IT Service and Architecture](https:\/\/www.simplilearn.com\/resources\/it-service-management)[Digital Marketing](https:\/\/www.simplilearn.com\/resources\/digital-marketing)[Big Data](https:\/\/www.simplilearn.com\/resources\/big-data-and-analytics)[Career Fast-track](https:\/\/www.simplilearn.com\/resources\/career-fast-track)[Enterprise](https:\/\/www.simplilearn.com\/resources\/enterprise)[Other Segments](https:\/\/www.simplilearn.com\/resources\/other-segments)\n\n[Articles](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics\/articles)[Ebooks](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics\/ebooks)[Free Practice Tests](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics\/free-practice-tests)[On-demand Webinars](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics\/on-demand-webinars)[Tutorials](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics\/tutorials)\n\nExplore our curated learning milestones for you\\!\n\n### Get Free Certifications with free video courses\n[View All](https:\/\/www.simplilearn.com\/skillup-search?sortBy=Relevance&tag=&category=Data%20Science%20&%20Business%20Analytics)\n\n![Business Analytics with Excel]()\n\n#### Data Science & Business Analytics\nBusiness Analytics with Excel\n\n4 hours4\\.6576K learners\n\n[Start Learning](https:\/\/www.simplilearn.com\/learn-business-analytics-excel-fundamentals-skillup?source=SidebarWidget_SkillUp)\n\n### Get Affiliated Certifications with Live Class programs\n[View All](https:\/\/www.simplilearn.com\/data-science-and-business-analytics\/)\n\n![Data Scientist](https:\/\/www.simplilearn.com\/ice9\/master_program\/masters-badge-2.svgz)\n\n#### Data Scientist\n- Earn an industry-recognized Data Science Master’s certificate from Simplilearn\n- Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more\n\n11 months\n\n[View Program]()\n\nClick here to close suggestions\\!\n\n[Home](https:\/\/www.simplilearn.com\/)[Resources](https:\/\/www.simplilearn.com\/resources)[Data Science & Business Analytics](https:\/\/www.simplilearn.com\/resources\/data-science-business-analytics)An Introduction to Exponential Smoothing for Time Series Forecasting in Python\n\n# An Introduction to Exponential Smoothing for Time Series Forecasting in Python\n[By Simplilearn](https:\/\/www.simplilearn.com\/authors\/simplilearn)\n\nShare This Article:\n\nLast updated on Sep 10, 202547208\n\n![An Introduction to Exponential Smoothing for Time Series Forecasting in Python](https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/Time_Series_Forecasting_in_Python.jpg)\n\nExponential smoothing is a widely preferred statistical technique to forecast a time series. We use this simple yet powerful forecasting method for smoothing univariate time series data by using the exponential window function.\n\n## What is Exponential Smoothing?\nExponential smoothing is a time series method for forecasting univariate time series data. [Time series](https:\/\/www.simplilearn.com\/tutorials\/python-tutorial\/time-series-analysis-in-python \"Time series\") methods work on the principle that a [prediction](https:\/\/www.simplilearn.com\/what-is-predictive-analytics-article \"prediction\") is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. It is called so because the weight assigned to each demand observation is exponentially decreased.\n\nThe model assumes that the future will be somewhat the same as the recent past. The only pattern that Exponential Smoothing learns from demand history is its level - the average value around which the demand varies over time.\n\nExponential smoothing is generally used to make forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends.\n\n## Exponential Smoothing Forecasting\nExponential smoothing is a broadly accurate forecasting method for short-term forecasts. The technique assigns larger weights to more recent observations while assigning exponentially decreasing weights as the observations get increasingly distant. This method produces slightly unreliable long-term forecasts.\n\nExponential smoothing can be most effective when the time series parameters vary slowly over time.\n\n## Types of Exponential Smoothing\nThe main types of Exponential Smoothing forecasting methods are:\n\n### 1\\. Simple or Single Exponential Smoothing\nSimple or single exponential smoothing (SES) is the method of time series forecasting used with univariate data with no trend and no seasonal pattern. It needs a single parameter called alpha (a), also known as the smoothing factor. Alpha controls the rate at which the influence of past observations decreases exponentially. The parameter is often set to a value between 0 and 1.\n\nThe simple exponential smoothing formula is given by:\n\nst = αxt+(1 – α)st-1= st-1+ α(xt – st-1)\n\nhere,\n\nst = smoothed statistic (simple weighted average of current observation xt)\n\nst-1 = previous smoothed statistic\n\nα = smoothing factor of data; 0 \\< α \\< 1\n\nt = time period\n\n### 2\\. Double Exponential Smoothing\nThis method is known as Holt's trend model or second-order exponential smoothing. Double exponential smoothing is used in time-series forecasting when the data has a linear trend but no seasonal pattern. The basic idea here is to introduce a term that can consider the possibility of the series exhibiting some trend.\n\nIn addition to the alpha parameter, Double exponential smoothing needs another smoothing\n\nfactor called beta (b), which controls the decay of the influence of change in trend. The method supports trends that change in additive ways (smoothing with linear trend) and trends that change in multiplicative ways (smoothing with exponential trend).\n\nThe Double exponential smoothing formulas are:\n\nS1 = x1\n\nB1 = x1-x0\n\nFor t\\>1,\n\nst = αxt + (1 – α)(st-1 + bt-1)\n\nβt = β(st – st-1) + (1 – β)bt-1\n\nhere,\n\nbt = best estimate of the trend at time t\n\nβ = trend smoothing factor; 0 \\< β \\<1\n\n### 3\\. Triple Exponential Smoothing\nThis method is the variation of exponential smoothing that's most advanced and is used for time series forecasting when the data has linear trends and seasonal patterns. The technique applies exponential smoothing three times – level smoothing, trend smoothing, and seasonal smoothing. A new smoothing parameter called gamma (g) is added to control the influence of the seasonal component.\n\nThe triple exponential smoothing method is called Holt-Winters Exponential Smoothing, named after its contributors, Charles Holt and Peter Winters.\n\nHolt-Winters Exponential Smoothing has two categories depending on the nature of the seasonal component:\n\n- Holt-Winter's Additive Method − for seasonality that is addictive.\n- Holt-Winter's Multiplicative Method – for seasonality that is multiplicative.\n\n## Become a Data Science & Business Analytics Professional\n- **28%**Annual Job Growth By 2026\n- **11\\.5 M**Expected New Jobs For Data Science By 2026\n\n- ![Data Scientist](https:\/\/www.simplilearn.com\/ice9\/master_program\/masters-badge-2.svgz)\n  \n  #### Data Scientist\n  - Earn an industry-recognized Data Science Master’s certificate from Simplilearn\n  - Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more\n  \n  11 months\n  \n  [View Program]()\n- ![Purdue University](https:\/\/www.simplilearn.com\/ice9\/university\/8th_SL_PG_1st_FOLD.svgz)\n  \n  #### Professional Certificate in Data Science and Generative AI\n  - Earn a program completion certificate from Simplilearn, in partnership with Purdue University\n  - Become eligible for Purdue University Alumni Association membership on program completion\n  \n  6 months\n  \n  [View Program]()\n\nprevNext\n\n### Here's what learners are saying regarding our programs:\n- ![A.Anthony Davis](https:\/\/www.simplilearn.com\/ice9\/reviews_images\/_1566794118.jpeg)\n  \n  #### A.Anthony Davis\n  Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent.\n- ![Magdalena Szarafin](https:\/\/www.simplilearn.com\/ice9\/free_resources_article_thumb\/Magdalena.jpg)\n  \n  #### Magdalena Szarafin\n  ##### Manager Group Accounting & Data Analytics, **Chemicals**\n  \n  My decision to upskill myself in data science from Simplilearn was a great choice. After completing my course, I was assigned many new projects to work on in my desired field of Data Analytics.\n\nprevNext\n\nNot sure what you’re looking for?[View all Related Programs](https:\/\/www.simplilearn.com\/data-science-and-business-analytics?source=InpageBannerCategory)\n\n## How to Configure Exponential Smoothing\nTo configure Exponential Smoothing, analysts need to specify all the model hyperparameters explicitly. However, this can be challenging for both beginners and experts.\n\nInstead, numerical optimization is commonly used to search for and fund the smoothing factors (alpha, beta, gamma, phi) for the model resulting in the most negligible error.\n\nAn exponential smoothing method can obtain values for unknown parameters by estimating them from the observed data. The initial values and unknown parameters can be estimated by minimizing the sum of the squared errors (SSE).\n\nThe parameters that indicate the kind of change in trend or seasonality (for example, whether they are additive or multiplicative or whether they should be dampened) need to be specified explicitly.\n\n## Exponential Smoothing in Python\nLet us look at how to implement exponential smoothing in [Python.](https:\/\/www.simplilearn.com\/learn-the-basics-of-python-article \"Python. \")\n\nThe Statsmodels Python library provides the implementations of Exponential Smoothing in Python.\n\n### Single Exponential Smoothing\nThe SimpleExpSmoothing Statsmodels class enables implementation of Single Exponential Smoothing or simple smoothing in Python.\n\nFirst, an instance of SimpleExpSmoothing is instantiated and passed the training data. Next, the fit() function is called, giving the fit configuration, especially the alpha value. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast.\n\n### Double and Triple Exponential Smoothing\nThe SimpleExpSmoothing Statsmodels class also enables the implementation of Double and Triple Exponential Smoothing in Python.\n\nFirst, an instance of SimpleExpSmoothing is instantiated, specifying training data and model configuration. We must define the configuration parameters for trend, damped, seasonal, and seasonal\\_periods. The fit() function is then called to fit the model on the training data.\n\nThe fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast.\n\n## Choose the Right Program\nLooking to build a career in the exciting field of data science? Our data science courses are designed to provide you with the skills and knowledge you need to excel in this rapidly growing industry. Our expert instructors will guide you through hands-on projects, real-world scenarios, and case studies, giving you the practical experience you need to succeed. With our courses, you'll learn to analyze data, create insightful reports, and make data-driven decisions that can help drive business success.\n\n## Conclusion\nIn this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method. To know more about time series forecasting, or to start a career in data science, consider our top-ranked [Professional Certificate in Data Science and Generative AI](https:\/\/www.simplilearn.com\/pgp-data-science-certification-bootcamp-program?source=GhPreviewCoursepages \"Professional Certificate in Data Science and Generative AI\") certification.\n\nUpskill yourself with our trending [Data Science courses](https:\/\/www.simplilearn.com\/data-science-and-business-analytics \"Data Science courses\") and certifications\n\n1. [Data Science Course](https:\/\/www.simplilearn.com\/data-science-course \"Data Science Course\")\n2. [Data Science Certificate Program](https:\/\/www.simplilearn.com\/pgp-data-science-certification-bootcamp-program \"Data Science Certificate Program\")\n3. [Applied Data Science with Python](https:\/\/www.simplilearn.com\/big-data-and-analytics\/python-for-data-science-training \"Applied Data Science with Python\")\n\n## FAQs\n### 1\\. What are the three types of exponential smoothing?\nThe three types of exponential smoothing are:\n\n- Simple or single exponential smoothing\n- Double exponential smoothing\n- Triple exponential smoothing\n### 2\\. What is meant by exponential smoothing?\nExponential smoothing is a method for forecasting univariate time series data. It is based on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. The technique is so called because the weight assigned to each demand observation exponentially decreases.\n\n### 3\\. Why is exponential smoothing best?\nExponential smoothing gives accurate and reliable forecasts to predict the next period. Analysts can analyze the projected and actual demand shown in the estimates for effective demand planning. This helps maintain accurate inventory levels.\n\nAdditionally, they can adjust exponential smoothing parameter values to change how quickly prior observations lose importance in calculations. This enables tweaking the relative significance of present observations to previous observations to meet the requirements of the subject area.\n\n### 4\\. How do you calculate exponential smoothing?\nThe simplest formula to calculate exponential smoothing is:\n\nst = αxt+(1 – α)st-1= st-1+ α(xt – st-1)\n\nhere,\n\nst = smoothed statistic (simple weighted average of current observation xt)\n\nst-1 = previous smoothed statistic\n\nα = smoothing factor of data; 0 \\< α \\< 1\n\nt = time period\n\n### 5\\. What is the difference between moving average and exponential smoothing?\nMoving Average and Exponential Smoothing are two important techniques used for time series forecasting.\n\nMoving Average is applied to data to filter random noise from it, while Exponential Smoothing applies exponential window function to data.\n\nMethods under the moving average smoothing process are focused on the values with their timings, while methods under exponential smoothing provide support against trend and seasonality components of time series. The exponential moving average is focused on current values.\n\nWe weigh past observations equally in Moving Average, while Exponential Smoothing assigns exponentially decreasing weights to observations as they age. To put it simply, recent observations are given more weightage in forecasting compared to older observations.\n\n### 6\\. How is exponential smoothing used in forecasting?\nExponential smoothing is a widely preferred forecasting method for smoothing univariate time series data using the exponential window function. The method works by assigning exponentially decreasing weights for past observations. Larger weights are assigned to more recent observations, while exponentially decreasing weights are assigned as the observations get more and more distant.\n\nExponential smoothing assumes that the future will be somewhat the same as the recent past and, therefore, provides forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. We can use it most effectively to make short-term forecasts when the time series parameters vary slowly over time.\n\n## Become a Data Science & Business Analytics Professional\n- **28%**Annual Job Growth By 2026\n- **11\\.5 M**Expected New Jobs For Data Science By 2026\n\n- ![Data Scientist](https:\/\/www.simplilearn.com\/ice9\/master_program\/masters-badge-2.svgz)\n  \n  #### Data Scientist\n  - Earn an industry-recognized Data Science Master’s certificate from Simplilearn\n  - Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more\n  \n  11 months\n  \n  [View Program]()\n- ![Purdue University](https:\/\/www.simplilearn.com\/ice9\/university\/8th_SL_PG_1st_FOLD.svgz)\n  \n  #### Professional Certificate in Data Science and Generative AI\n  - Earn a program completion certificate from Simplilearn, in partnership with Purdue University\n  - Become eligible for Purdue University Alumni Association membership on program completion\n  \n  6 months\n  \n  [View Program]()\n\nprevNext\n\n### Here's what learners are saying regarding our programs:\n- ![A.Anthony Davis](https:\/\/www.simplilearn.com\/ice9\/reviews_images\/_1566794118.jpeg)\n  \n  #### A.Anthony Davis\n  Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. 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Meta Title	An Introduction to Exponential Smoothing for Time Series Forecasting in Python \| Simplilearn
Meta Description	In this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method.
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Boilerpipe Text	Exponential smoothing is a widely preferred statistical technique to forecast a time series. We use this simple yet powerful forecasting method for smoothing univariate time series data by using the exponential window function. What is Exponential Smoothing? Exponential smoothing is a time series method for forecasting univariate time series data. Time series methods work on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. It is called so because the weight assigned to each demand observation is exponentially decreased. The model assumes that the future will be somewhat the same as the recent past. The only pattern that Exponential Smoothing learns from demand history is its level - the average value around which the demand varies over time. Exponential smoothing is generally used to make forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. Exponential Smoothing Forecasting Exponential smoothing is a broadly accurate forecasting method for short-term forecasts. The technique assigns larger weights to more recent observations while assigning exponentially decreasing weights as the observations get increasingly distant. This method produces slightly unreliable long-term forecasts. Exponential smoothing can be most effective when the time series parameters vary slowly over time. Types of Exponential Smoothing The main types of Exponential Smoothing forecasting methods are: 1. Simple or Single Exponential Smoothing Simple or single exponential smoothing (SES) is the method of time series forecasting used with univariate data with no trend and no seasonal pattern. It needs a single parameter called alpha (a), also known as the smoothing factor. Alpha controls the rate at which the influence of past observations decreases exponentially. The parameter is often set to a value between 0 and 1. The simple exponential smoothing formula is given by: st = αxt+(1 – α)st-1= st-1+ α(xt – st-1) here, st = smoothed statistic (simple weighted average of current observation xt) st-1 = previous smoothed statistic α = smoothing factor of data; 0 < α < 1 t = time period 2. Double Exponential Smoothing This method is known as Holt's trend model or second-order exponential smoothing. Double exponential smoothing is used in time-series forecasting when the data has a linear trend but no seasonal pattern. The basic idea here is to introduce a term that can consider the possibility of the series exhibiting some trend. In addition to the alpha parameter, Double exponential smoothing needs another smoothing factor called beta (b), which controls the decay of the influence of change in trend. The method supports trends that change in additive ways (smoothing with linear trend) and trends that change in multiplicative ways (smoothing with exponential trend). The Double exponential smoothing formulas are: S1 = x1 B1 = x1-x0 For t>1, st = αxt + (1 – α)(st-1 + bt-1) βt = β(st – st-1) + (1 – β)bt-1 here, bt = best estimate of the trend at time t β = trend smoothing factor; 0 < β <1 3. Triple Exponential Smoothing This method is the variation of exponential smoothing that's most advanced and is used for time series forecasting when the data has linear trends and seasonal patterns. The technique applies exponential smoothing three times – level smoothing, trend smoothing, and seasonal smoothing. A new smoothing parameter called gamma (g) is added to control the influence of the seasonal component. The triple exponential smoothing method is called Holt-Winters Exponential Smoothing, named after its contributors, Charles Holt and Peter Winters. Holt-Winters Exponential Smoothing has two categories depending on the nature of the seasonal component: Holt-Winter's Additive Method − for seasonality that is addictive. Holt-Winter's Multiplicative Method – for seasonality that is multiplicative. How to Configure Exponential Smoothing To configure Exponential Smoothing, analysts need to specify all the model hyperparameters explicitly. However, this can be challenging for both beginners and experts. Instead, numerical optimization is commonly used to search for and fund the smoothing factors (alpha, beta, gamma, phi) for the model resulting in the most negligible error. An exponential smoothing method can obtain values for unknown parameters by estimating them from the observed data. The initial values and unknown parameters can be estimated by minimizing the sum of the squared errors (SSE). The parameters that indicate the kind of change in trend or seasonality (for example, whether they are additive or multiplicative or whether they should be dampened) need to be specified explicitly. Exponential Smoothing in Python Let us look at how to implement exponential smoothing in Python. The Statsmodels Python library provides the implementations of Exponential Smoothing in Python. Single Exponential Smoothing The SimpleExpSmoothing Statsmodels class enables implementation of Single Exponential Smoothing or simple smoothing in Python. First, an instance of SimpleExpSmoothing is instantiated and passed the training data. Next, the fit() function is called, giving the fit configuration, especially the alpha value. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast. Double and Triple Exponential Smoothing The SimpleExpSmoothing Statsmodels class also enables the implementation of Double and Triple Exponential Smoothing in Python. First, an instance of SimpleExpSmoothing is instantiated, specifying training data and model configuration. We must define the configuration parameters for trend, damped, seasonal, and seasonal_periods. The fit() function is then called to fit the model on the training data. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast. Choose the Right Program Looking to build a career in the exciting field of data science? Our data science courses are designed to provide you with the skills and knowledge you need to excel in this rapidly growing industry. Our expert instructors will guide you through hands-on projects, real-world scenarios, and case studies, giving you the practical experience you need to succeed. With our courses, you'll learn to analyze data, create insightful reports, and make data-driven decisions that can help drive business success. Conclusion In this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method. To know more about time series forecasting, or to start a career in data science, consider our top-ranked Professional Certificate in Data Science and Generative AI certification. Upskill yourself with our trending Data Science courses and certifications Data Science Course Data Science Certificate Program Applied Data Science with Python FAQs 1. What are the three types of exponential smoothing? The three types of exponential smoothing are: Simple or single exponential smoothing Double exponential smoothing Triple exponential smoothing 2. What is meant by exponential smoothing? Exponential smoothing is a method for forecasting univariate time series data. It is based on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. The technique is so called because the weight assigned to each demand observation exponentially decreases. 3. Why is exponential smoothing best? Exponential smoothing gives accurate and reliable forecasts to predict the next period. Analysts can analyze the projected and actual demand shown in the estimates for effective demand planning. This helps maintain accurate inventory levels. Additionally, they can adjust exponential smoothing parameter values to change how quickly prior observations lose importance in calculations. This enables tweaking the relative significance of present observations to previous observations to meet the requirements of the subject area. 4. How do you calculate exponential smoothing? The simplest formula to calculate exponential smoothing is: st = αxt+(1 – α)st-1= st-1+ α(xt – st-1) here, st = smoothed statistic (simple weighted average of current observation xt) st-1 = previous smoothed statistic α = smoothing factor of data; 0 < α < 1 t = time period 5. What is the difference between moving average and exponential smoothing? Moving Average and Exponential Smoothing are two important techniques used for time series forecasting. Moving Average is applied to data to filter random noise from it, while Exponential Smoothing applies exponential window function to data. Methods under the moving average smoothing process are focused on the values with their timings, while methods under exponential smoothing provide support against trend and seasonality components of time series. The exponential moving average is focused on current values. We weigh past observations equally in Moving Average, while Exponential Smoothing assigns exponentially decreasing weights to observations as they age. To put it simply, recent observations are given more weightage in forecasting compared to older observations. 6. How is exponential smoothing used in forecasting? Exponential smoothing is a widely preferred forecasting method for smoothing univariate time series data using the exponential window function. The method works by assigning exponentially decreasing weights for past observations. Larger weights are assigned to more recent observations, while exponentially decreasing weights are assigned as the observations get more and more distant. Exponential smoothing assumes that the future will be somewhat the same as the recent past and, therefore, provides forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. We can use it most effectively to make short-term forecasts when the time series parameters vary slowly over time.
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[Home](https://www.simplilearn.com/)[Resources](https://www.simplilearn.com/resources)[Data Science & Business Analytics](https://www.simplilearn.com/resources/data-science-business-analytics)An Introduction to Exponential Smoothing for Time Series Forecasting in Python # An Introduction to Exponential Smoothing for Time Series Forecasting in Python [By Simplilearn](https://www.simplilearn.com/authors/simplilearn) Share This Article: Last updated on Sep 10, 202547208 ![An Introduction to Exponential Smoothing for Time Series Forecasting in Python](https://www.simplilearn.com/ice9/free_resources_article_thumb/Time_Series_Forecasting_in_Python.jpg) Exponential smoothing is a widely preferred statistical technique to forecast a time series. We use this simple yet powerful forecasting method for smoothing univariate time series data by using the exponential window function. ## What is Exponential Smoothing? Exponential smoothing is a time series method for forecasting univariate time series data. [Time series](https://www.simplilearn.com/tutorials/python-tutorial/time-series-analysis-in-python "Time series") methods work on the principle that a [prediction](https://www.simplilearn.com/what-is-predictive-analytics-article "prediction") is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. It is called so because the weight assigned to each demand observation is exponentially decreased. The model assumes that the future will be somewhat the same as the recent past. The only pattern that Exponential Smoothing learns from demand history is its level - the average value around which the demand varies over time. Exponential smoothing is generally used to make forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. ## Exponential Smoothing Forecasting Exponential smoothing is a broadly accurate forecasting method for short-term forecasts. The technique assigns larger weights to more recent observations while assigning exponentially decreasing weights as the observations get increasingly distant. This method produces slightly unreliable long-term forecasts. Exponential smoothing can be most effective when the time series parameters vary slowly over time. ## Types of Exponential Smoothing The main types of Exponential Smoothing forecasting methods are: ### 1\. Simple or Single Exponential Smoothing Simple or single exponential smoothing (SES) is the method of time series forecasting used with univariate data with no trend and no seasonal pattern. It needs a single parameter called alpha (a), also known as the smoothing factor. Alpha controls the rate at which the influence of past observations decreases exponentially. The parameter is often set to a value between 0 and 1. The simple exponential smoothing formula is given by: st = αxt+(1 – α)st-1= st-1+ α(xt – st-1) here, st = smoothed statistic (simple weighted average of current observation xt) st-1 = previous smoothed statistic α = smoothing factor of data; 0 \< α \< 1 t = time period ### 2\. Double Exponential Smoothing This method is known as Holt's trend model or second-order exponential smoothing. Double exponential smoothing is used in time-series forecasting when the data has a linear trend but no seasonal pattern. The basic idea here is to introduce a term that can consider the possibility of the series exhibiting some trend. In addition to the alpha parameter, Double exponential smoothing needs another smoothing factor called beta (b), which controls the decay of the influence of change in trend. The method supports trends that change in additive ways (smoothing with linear trend) and trends that change in multiplicative ways (smoothing with exponential trend). The Double exponential smoothing formulas are: S1 = x1 B1 = x1-x0 For t\>1, st = αxt + (1 – α)(st-1 + bt-1) βt = β(st – st-1) + (1 – β)bt-1 here, bt = best estimate of the trend at time t β = trend smoothing factor; 0 \< β \<1 ### 3\. Triple Exponential Smoothing This method is the variation of exponential smoothing that's most advanced and is used for time series forecasting when the data has linear trends and seasonal patterns. The technique applies exponential smoothing three times – level smoothing, trend smoothing, and seasonal smoothing. A new smoothing parameter called gamma (g) is added to control the influence of the seasonal component. The triple exponential smoothing method is called Holt-Winters Exponential Smoothing, named after its contributors, Charles Holt and Peter Winters. Holt-Winters Exponential Smoothing has two categories depending on the nature of the seasonal component: - Holt-Winter's Additive Method − for seasonality that is addictive. - Holt-Winter's Multiplicative Method – for seasonality that is multiplicative. ## Become a Data Science & Business Analytics Professional - 28%Annual Job Growth By 2026 - 11\.5 MExpected New Jobs For Data Science By 2026 - ![Data Scientist](https://www.simplilearn.com/ice9/master_program/masters-badge-2.svgz) #### Data Scientist - Earn an industry-recognized Data Science Master’s certificate from Simplilearn - Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more 11 months [View Program]() - ![Purdue University](https://www.simplilearn.com/ice9/university/8th_SL_PG_1st_FOLD.svgz) #### Professional Certificate in Data Science and Generative AI - Earn a program completion certificate from Simplilearn, in partnership with Purdue University - Become eligible for Purdue University Alumni Association membership on program completion 6 months [View Program]() prevNext ### Here's what learners are saying regarding our programs: - ![A.Anthony Davis](https://www.simplilearn.com/ice9/reviews_images/_1566794118.jpeg) #### A.Anthony Davis Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent. - ![Magdalena Szarafin](https://www.simplilearn.com/ice9/free_resources_article_thumb/Magdalena.jpg) #### Magdalena Szarafin ##### Manager Group Accounting & Data Analytics, Chemicals My decision to upskill myself in data science from Simplilearn was a great choice. After completing my course, I was assigned many new projects to work on in my desired field of Data Analytics. prevNext Not sure what you’re looking for?[View all Related Programs](https://www.simplilearn.com/data-science-and-business-analytics?source=InpageBannerCategory) ## How to Configure Exponential Smoothing To configure Exponential Smoothing, analysts need to specify all the model hyperparameters explicitly. However, this can be challenging for both beginners and experts. Instead, numerical optimization is commonly used to search for and fund the smoothing factors (alpha, beta, gamma, phi) for the model resulting in the most negligible error. An exponential smoothing method can obtain values for unknown parameters by estimating them from the observed data. The initial values and unknown parameters can be estimated by minimizing the sum of the squared errors (SSE). The parameters that indicate the kind of change in trend or seasonality (for example, whether they are additive or multiplicative or whether they should be dampened) need to be specified explicitly. ## Exponential Smoothing in Python Let us look at how to implement exponential smoothing in [Python.](https://www.simplilearn.com/learn-the-basics-of-python-article "Python. ") The Statsmodels Python library provides the implementations of Exponential Smoothing in Python. ### Single Exponential Smoothing The SimpleExpSmoothing Statsmodels class enables implementation of Single Exponential Smoothing or simple smoothing in Python. First, an instance of SimpleExpSmoothing is instantiated and passed the training data. Next, the fit() function is called, giving the fit configuration, especially the alpha value. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast. ### Double and Triple Exponential Smoothing The SimpleExpSmoothing Statsmodels class also enables the implementation of Double and Triple Exponential Smoothing in Python. First, an instance of SimpleExpSmoothing is instantiated, specifying training data and model configuration. We must define the configuration parameters for trend, damped, seasonal, and seasonal\_periods. The fit() function is then called to fit the model on the training data. The fit() function returns an instance of the HoltWintersResults class containing the learned coefficients. The forecast() or the predict() function is then called on the result object to make a forecast. ## Choose the Right Program Looking to build a career in the exciting field of data science? Our data science courses are designed to provide you with the skills and knowledge you need to excel in this rapidly growing industry. Our expert instructors will guide you through hands-on projects, real-world scenarios, and case studies, giving you the practical experience you need to succeed. With our courses, you'll learn to analyze data, create insightful reports, and make data-driven decisions that can help drive business success. ## Conclusion In this tutorial, you learned the introduction to exponential smoothing for time series forecasting in Python, its types, and how to implement the method. To know more about time series forecasting, or to start a career in data science, consider our top-ranked [Professional Certificate in Data Science and Generative AI](https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?source=GhPreviewCoursepages "Professional Certificate in Data Science and Generative AI") certification. Upskill yourself with our trending [Data Science courses](https://www.simplilearn.com/data-science-and-business-analytics "Data Science courses") and certifications 1. [Data Science Course](https://www.simplilearn.com/data-science-course "Data Science Course") 2. [Data Science Certificate Program](https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program "Data Science Certificate Program") 3. [Applied Data Science with Python](https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training "Applied Data Science with Python") ## FAQs ### 1\. What are the three types of exponential smoothing? The three types of exponential smoothing are: - Simple or single exponential smoothing - Double exponential smoothing - Triple exponential smoothing ### 2\. What is meant by exponential smoothing? Exponential smoothing is a method for forecasting univariate time series data. It is based on the principle that a prediction is a weighted linear sum of past observations or lags. The Exponential Smoothing time series method works by assigning exponentially decreasing weights for past observations. The technique is so called because the weight assigned to each demand observation exponentially decreases. ### 3\. Why is exponential smoothing best? Exponential smoothing gives accurate and reliable forecasts to predict the next period. Analysts can analyze the projected and actual demand shown in the estimates for effective demand planning. This helps maintain accurate inventory levels. Additionally, they can adjust exponential smoothing parameter values to change how quickly prior observations lose importance in calculations. This enables tweaking the relative significance of present observations to previous observations to meet the requirements of the subject area. ### 4\. How do you calculate exponential smoothing? The simplest formula to calculate exponential smoothing is: st = αxt+(1 – α)st-1= st-1+ α(xt – st-1) here, st = smoothed statistic (simple weighted average of current observation xt) st-1 = previous smoothed statistic α = smoothing factor of data; 0 \< α \< 1 t = time period ### 5\. What is the difference between moving average and exponential smoothing? Moving Average and Exponential Smoothing are two important techniques used for time series forecasting. Moving Average is applied to data to filter random noise from it, while Exponential Smoothing applies exponential window function to data. Methods under the moving average smoothing process are focused on the values with their timings, while methods under exponential smoothing provide support against trend and seasonality components of time series. The exponential moving average is focused on current values. We weigh past observations equally in Moving Average, while Exponential Smoothing assigns exponentially decreasing weights to observations as they age. To put it simply, recent observations are given more weightage in forecasting compared to older observations. ### 6\. How is exponential smoothing used in forecasting? Exponential smoothing is a widely preferred forecasting method for smoothing univariate time series data using the exponential window function. The method works by assigning exponentially decreasing weights for past observations. Larger weights are assigned to more recent observations, while exponentially decreasing weights are assigned as the observations get more and more distant. Exponential smoothing assumes that the future will be somewhat the same as the recent past and, therefore, provides forecasts of time-series data based on prior assumptions by the user, such as seasonality or systematic trends. We can use it most effectively to make short-term forecasts when the time series parameters vary slowly over time. ## Become a Data Science & Business Analytics Professional - 28%Annual Job Growth By 2026 - 11\.5 MExpected New Jobs For Data Science By 2026 - ![Data Scientist](https://www.simplilearn.com/ice9/master_program/masters-badge-2.svgz) #### Data Scientist - Earn an industry-recognized Data Science Master’s certificate from Simplilearn - Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more 11 months [View Program]() - ![Purdue University](https://www.simplilearn.com/ice9/university/8th_SL_PG_1st_FOLD.svgz) #### Professional Certificate in Data Science and Generative AI - Earn a program completion certificate from Simplilearn, in partnership with Purdue University - Become eligible for Purdue University Alumni Association membership on program completion 6 months [View Program]() prevNext ### Here's what learners are saying regarding our programs: - ![A.Anthony Davis](https://www.simplilearn.com/ice9/reviews_images/_1566794118.jpeg) #### A.Anthony Davis Simplilearn United States has one of the best programs available online to earn real-world skills that are in demand worldwide. I just completed the Machine Learning Advanced course, and the LMS was excellent. - ![Magdalena Szarafin](https://www.simplilearn.com/ice9/free_resources_article_thumb/Magdalena.jpg) #### Magdalena Szarafin ##### Manager Group Accounting & Data Analytics, Chemicals My decision to upskill myself in data science from Simplilearn was a great choice. After completing my course, I was assigned many new projects to work on in my desired field of Data Analytics. prevNext Not sure what you’re looking for?[View all Related Programs](https://www.simplilearn.com/data-science-and-business-analytics?source=InpageBannerCategory) ### Get Free Certifications with free video courses - ![Business Analytics with Excel](https://www.simplilearn.com/ice9/app/Business-Analytics-with-Excel_288X288.jpg) #### Data Science & Business Analytics Business Analytics with Excel 4 hours4\.6576K learners [Start Learning](https://www.simplilearn.com/learn-business-analytics-excel-fundamentals-skillup?source=BBanner_Skillup) - ![Power BI for Beginners](https://www.simplilearn.com/ice9/app/PowerBI_288x288.jpg) #### Data Science & Business Analytics Power BI for Beginners 6 hours4\.6368\.5K learners [Start Learning](https://www.simplilearn.com/learn-power-bi-basics-free-course-skillup?source=BBanner_Skillup) - ![Business Intelligence Fundamentals](https://www.simplilearn.com/ice9/assets/form_opacity.png) #### Data Science & Business Analytics Business Intelligence Fundamentals 10 hours4\.640K learners [Start Learning](https://www.simplilearn.com/free-business-intelligence-course-online-skillup?source=BBanner_Skillup) prevNext ## Recommended Reads - [Top AI Jobs & How to Land Them in 202613 Oct, 2025](https://www.simplilearn.com/top-ai-job-and-how-to-land-them-pdf) - [Data Scientist or Data Engineer: Educational and Career Opportunities545329 Jun, 2025](https://www.simplilearn.com/data-scientist-or-data-engineer-article) - [How to Become a Data Scientist in 20263706336 Nov, 2025](https://www.simplilearn.com/tutorials/data-science-tutorial/how-to-become-a-data-scientist) - [GenAI in the Fast Lane - A Guide to Turbocharge Your Organization’s Capability28 Jan, 2025](https://www.simplilearn.com/guide-to-turbocharge-your-team-gen-ai-capabilities-pdf) - [A Day in the Life of a Data Scientist9250128 Dec, 2025](https://www.simplilearn.com/a-day-in-the-life-of-a-data-scientist-article) - [Data Analyst vs. Data Scientist17649820 Nov, 2025](https://www.simplilearn.com/tutorials/data-analytics-tutorial/data-analyst-vs-data-scientist) prevNext ### Get Free Certifications with free video courses ![Business Analytics with Excel](https://www.simplilearn.com/ice9/app/Business-Analytics-with-Excel_288X288.jpg) #### Data Science & Business Analytics Business Analytics with Excel 4 hours4\.6576K learners [Start Learning](https://www.simplilearn.com/learn-business-analytics-excel-fundamentals-skillup?source=SidebarWidget_SkillUp) ![Power BI for Beginners](https://www.simplilearn.com/ice9/app/PowerBI_288x288.jpg) #### Data Science & Business Analytics Power BI for Beginners 6 hours4\.6368\.5K learners [Start Learning](https://www.simplilearn.com/learn-power-bi-basics-free-course-skillup?source=SidebarWidget_SkillUp) ![Business Intelligence Fundamentals](https://www.simplilearn.com/ice9/app/BusinessIntelligenceFundamentals_288x288.jpg) #### Data Science & Business Analytics Business Intelligence Fundamentals 10 hours4\.640K learners [Start Learning](https://www.simplilearn.com/free-business-intelligence-course-online-skillup?source=SidebarWidget_SkillUp) ### Get Affiliated Certifications with Live Class programs ![Data Scientist](https://www.simplilearn.com/ice9/master_program/masters-badge-2.svgz) #### Data Scientist - Earn an industry-recognized Data Science Master’s certificate from Simplilearn - Learn through an AI-driven curriculum covering Python, SQL, advanced statistics, ML,DNNs and more 11 months [View Program]() ![Purdue University](https://www.simplilearn.com/ice9/university/8th_SL_PG_1st_FOLD.svgz) #### Professional Certificate in Data Science and Generative AI - Earn a program completion certificate from Simplilearn, in partnership with Purdue University - Become eligible for Purdue University Alumni Association membership on program completion 6 months [View Program]() © 2009 -2026- Simplilearn Solutions. 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