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1. Shard Calculation

Query:

Response:

Calculated Shard: 153 (from laksa152)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa153.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://johngalt.zendesk.com/hc/en-us/articles/360021754152-Simple-Exponential-Smoothing\')), getAhrefsUnparsedNoserviceFromURL(\'https://johngalt.zendesk.com/hc/en-us/articles/360021754152-Simple-Exponential-Smoothing\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021754152-Simple-Exponential-Smoothing","crawl_time":1774524236,"first_indexed_time":1584916758,"http_code":200,"src_unparsed":"com,zendesk!johngalt,\/hc\/en-us\/articles\/360021754152-Simple-Exponential-Smoothing s443","src_root_hash":"11136510553130863353","history_drop_reason":null,"meta_title":"Simple Exponential Smoothing – John Galt Solutions","meta_descriptions":["Simple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives..."],"attrs_boilerpipe_text":"Overview\nAdaptive Exponential Smoothing\nBass Model\nBox-Jenkins\nCensus X-11\nCroston Intermittent\nDecomposition\nDouble Simple Exponential Smoothing Brown\nDouble Exponential Smoothing Holt\nErlang B\nSee more\nFollow\nSimple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast.\nDepending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values.\nThe level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value.\nTo use the Simple Exponential Smoothing forecasting method:\nClick \n and open the ForecastingExamples.xls file.\nNote: \nThe ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available.\nClick the\nSimple Exp. Smoothing\ntab at the bottom of the Excel sheet to select the example spreadsheet. \nClick in a cell containing data and open ForecastX™ by clicking \n. ForecastX appears. \nClick the\nForecast Method\ntab.\nFrom the Forecast Technique menu, scroll through the list of methods and select\nSimple Exponential Smoothing\n. The Simple Exponential Smoothing Forecasting technique appears.\nEnable the\nEdit Parameters\ncheckbox to activate Simple Exponential Smoothing’s parameters.\nIn the Smoothing Constants section, enter the Level. \nOn the Reports tab, enable the\nAudit\ncheckbox.\nNote: \nThe Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast.\nClick\nFinish\n. \nForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart.\nOne line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values.\nForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast.\nYou can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing.\nRelated articles\nSlow Moving Demand\nDouble Exponential Smoothing Holt\nDouble Simple Exponential Smoothing Brown\nWeighted Moving Average\nEvent Model","attrs_markdown":"[![John Galt Solutions Help Center home page](https:\/\/johngalt.zendesk.com\/hc\/theming_assets\/01JNPMK8R7H0JBZ0D9F133BC06)](https:\/\/johngalt.zendesk.com\/hc\/en-us \"Home\")\n\n[Submit a request](https:\/\/johngalt.zendesk.com\/hc\/en-us\/requests\/new)\n\n[Sign in](https:\/\/johngalt.zendesk.com\/access?locale=en-us&brand_id=360001924051&return_to=https%3A%2F%2Fjohngalt.zendesk.com%2Fhc%2Fen-us%2Farticles%2F360021754152-Simple-Exponential-Smoothing&locale=en-us \"Opens a dialog\")\n\n1. [John Galt Solutions](https:\/\/johngalt.zendesk.com\/hc\/en-us)\n2. [ForecastX](https:\/\/johngalt.zendesk.com\/hc\/en-us\/categories\/360001318071-ForecastX)\n3. [Forecast Methods](https:\/\/johngalt.zendesk.com\/hc\/en-us\/sections\/360003390211-Forecast-Methods)\n\n### Articles in this section\n- [Overview](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021961651-Overview)\n- [Adaptive Exponential Smoothing](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021743312-Adaptive-Exponential-Smoothing)\n- [Bass Model](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021743732-Bass-Model)\n- [Box-Jenkins](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021744612-Box-Jenkins)\n- [Census X-11](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021966211-Census-X-11)\n- [Croston Intermittent](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021746832-Croston-Intermittent)\n- [Decomposition](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967591-Decomposition)\n- [Double Simple Exponential Smoothing Brown](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967611-Double-Simple-Exponential-Smoothing-Brown)\n- [Double Exponential Smoothing Holt](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967791-Double-Exponential-Smoothing-Holt)\n- [Erlang B](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021968131-Erlang-B)\n\n[See more](https:\/\/johngalt.zendesk.com\/hc\/en-us\/sections\/360003390211-Forecast-Methods)\n\n# Simple Exponential Smoothing\n- August 20, 2019 22:27\n- Updated\n\n[Follow](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021754152-Simple-Exponential-Smoothing\/subscription.html \"Opens a sign-in dialog\")\n\nSimple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast.\n\nDepending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values.\n\nThe level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value.\n\nTo use the Simple Exponential Smoothing forecasting method:\n\n1. Click ![](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360018688151\/mceclip0.png) and open the ForecastingExamples.xls file.  \n   **Note:** The ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available.\n2. Click the **Simple Exp. Smoothing** tab at the bottom of the Excel sheet to select the example spreadsheet.\n3. Click in a cell containing data and open ForecastX™ by clicking ![](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360018813312\/mceclip1.png). ForecastX appears.\n4. Click the **Forecast Method** tab.\n5. From the Forecast Technique menu, scroll through the list of methods and select **Simple Exponential Smoothing**. The Simple Exponential Smoothing Forecasting technique appears.  \n   ![simple\\_exp\\_smoothing.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360027475692\/simple_exp_smoothing.png)\n6. Enable the **Edit Parameters** checkbox to activate Simple Exponential Smoothing’s parameters.\n7. In the Smoothing Constants section, enter the Level.\n8. On the Reports tab, enable the **Audit** checkbox.   \n   **Note:** The Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast.\n9. Click **Finish**.\n\nForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart.\n\nOne line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values.\n\n![mceclip0.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360036623092\/mceclip0.png)\n\nForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast.\n\n![mceclip1.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360036623172\/mceclip1.png)\n\nYou can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing.\n\n© 2026 John Galt Solutions. All rights reserved. Confidential do not distribute.\n\nWas this article helpful?\n\n0 out of 0 found this helpful\n\nHave more questions? [Submit a request](https:\/\/johngalt.zendesk.com\/hc\/en-us\/requests\/new)\n\n[Return to top](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021754152-Simple-Exponential-Smoothing#article-container)\n\n### Related articles\n- [Slow Moving Demand](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCKgD%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI3L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NDc5Mi1TbG93LU1vdmluZy1EZW1hbmQGOwhUOglyYW5raQY%3D--9b633b803b0feb35906196d8a6a573fd535fdcca)\n- [Double Exponential Smoothing Holt](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCK9D%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJGL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2Nzc5MS1Eb3VibGUtRXhwb25lbnRpYWwtU21vb3RoaW5nLUhvbHQGOwhUOglyYW5raQc%3D--20d0ee26e5234e2923418944e4965328c28504c7)\n- [Double Simple Exponential Smoothing Brown](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCPtC%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJOL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2NzYxMS1Eb3VibGUtU2ltcGxlLUV4cG9uZW50aWFsLVNtb290aGluZy1Ccm93bgY7CFQ6CXJhbmtpCA%3D%3D--0d0edb317064f9392cc0d76d967c2c5a3fdb3faa)\n- [Weighted Moving Average](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCBAK%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI8L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NjQzMi1XZWlnaHRlZC1Nb3ZpbmctQXZlcmFnZQY7CFQ6CXJhbmtpCQ%3D%3D--5f7fe6d3bc7546d1c414e758cc887051d736ef32)\n- [Event Model](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCNzy99JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSIwL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1MDQ5Mi1FdmVudC1Nb2RlbAY7CFQ6CXJhbmtpCg%3D%3D--199fcdca811581c20039c6a35b5d8297f7caa1e4)\n\n### Comments\n0 comments\n\nPlease [sign in](https:\/\/johngalt.zendesk.com\/access?locale=en-us&brand_id=360001924051&return_to=https%3A%2F%2Fjohngalt.zendesk.com%2Fhc%2Fen-us%2Farticles%2F360021754152-Simple-Exponential-Smoothing) to leave a comment.\n\n© 2026 John Galt Solutions. All rights reserved. Confidential do not distribute.","attrs_readable_markdown":"- [Overview](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021961651-Overview)\n- [Adaptive Exponential Smoothing](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021743312-Adaptive-Exponential-Smoothing)\n- [Bass Model](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021743732-Bass-Model)\n- [Box-Jenkins](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021744612-Box-Jenkins)\n- [Census X-11](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021966211-Census-X-11)\n- [Croston Intermittent](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021746832-Croston-Intermittent)\n- [Decomposition](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967591-Decomposition)\n- [Double Simple Exponential Smoothing Brown](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967611-Double-Simple-Exponential-Smoothing-Brown)\n- [Double Exponential Smoothing Holt](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021967791-Double-Exponential-Smoothing-Holt)\n- [Erlang B](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021968131-Erlang-B)\n\n[See more](https:\/\/johngalt.zendesk.com\/hc\/en-us\/sections\/360003390211-Forecast-Methods)\n\n[Follow](https:\/\/johngalt.zendesk.com\/hc\/en-us\/articles\/360021754152-Simple-Exponential-Smoothing\/subscription.html \"Opens a sign-in dialog\")\n\nSimple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast.\n\nDepending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values.\n\nThe level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value.\n\nTo use the Simple Exponential Smoothing forecasting method:\n\n1. Click ![](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360018688151\/mceclip0.png) and open the ForecastingExamples.xls file.  \n   **Note:** The ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available.\n2. Click the **Simple Exp. Smoothing** tab at the bottom of the Excel sheet to select the example spreadsheet.\n3. Click in a cell containing data and open ForecastX™ by clicking ![](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360018813312\/mceclip1.png). ForecastX appears.\n4. Click the **Forecast Method** tab.\n5. From the Forecast Technique menu, scroll through the list of methods and select **Simple Exponential Smoothing**. The Simple Exponential Smoothing Forecasting technique appears.  \n   ![simple\\_exp\\_smoothing.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360027475692\/simple_exp_smoothing.png)\n6. Enable the **Edit Parameters** checkbox to activate Simple Exponential Smoothing’s parameters.\n7. In the Smoothing Constants section, enter the Level.\n8. On the Reports tab, enable the **Audit** checkbox.   \n   **Note:** The Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast.\n9. Click **Finish**.\n\nForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart.\n\nOne line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values.\n\n![mceclip0.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360036623092\/mceclip0.png)\n\nForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast.\n\n![mceclip1.png](https:\/\/johngalt.zendesk.com\/hc\/article_attachments\/360036623172\/mceclip1.png)\n\nYou can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing.\n\n### Related articles\n- [Slow Moving Demand](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCKgD%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI3L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NDc5Mi1TbG93LU1vdmluZy1EZW1hbmQGOwhUOglyYW5raQY%3D--9b633b803b0feb35906196d8a6a573fd535fdcca)\n- [Double Exponential Smoothing Holt](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCK9D%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJGL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2Nzc5MS1Eb3VibGUtRXhwb25lbnRpYWwtU21vb3RoaW5nLUhvbHQGOwhUOglyYW5raQc%3D--20d0ee26e5234e2923418944e4965328c28504c7)\n- [Double Simple Exponential Smoothing Brown](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCPtC%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJOL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2NzYxMS1Eb3VibGUtU2ltcGxlLUV4cG9uZW50aWFsLVNtb290aGluZy1Ccm93bgY7CFQ6CXJhbmtpCA%3D%3D--0d0edb317064f9392cc0d76d967c2c5a3fdb3faa)\n- [Weighted Moving Average](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCBAK%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI8L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NjQzMi1XZWlnaHRlZC1Nb3ZpbmctQXZlcmFnZQY7CFQ6CXJhbmtpCQ%3D%3D--5f7fe6d3bc7546d1c414e758cc887051d736ef32)\n- [Event Model](https:\/\/johngalt.zendesk.com\/hc\/en-us\/related\/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCNzy99JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSIwL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1MDQ5Mi1FdmVudC1Nb2RlbAY7CFQ6CXJhbmtpCg%3D%3D--199fcdca811581c20039c6a35b5d8297f7caa1e4)","meta_canonical":null}

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Boilerpipe Text	Overview Adaptive Exponential Smoothing Bass Model Box-Jenkins Census X-11 Croston Intermittent Decomposition Double Simple Exponential Smoothing Brown Double Exponential Smoothing Holt Erlang B See more Follow Simple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast. Depending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values. The level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value. To use the Simple Exponential Smoothing forecasting method: Click and open the ForecastingExamples.xls file. Note: The ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available. Click the Simple Exp. Smoothing tab at the bottom of the Excel sheet to select the example spreadsheet. Click in a cell containing data and open ForecastX™ by clicking . ForecastX appears. Click the Forecast Method tab. From the Forecast Technique menu, scroll through the list of methods and select Simple Exponential Smoothing . The Simple Exponential Smoothing Forecasting technique appears. Enable the Edit Parameters checkbox to activate Simple Exponential Smoothing’s parameters. In the Smoothing Constants section, enter the Level. On the Reports tab, enable the Audit checkbox. Note: The Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast. Click Finish . ForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart. One line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values. ForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast. You can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing. Related articles Slow Moving Demand Double Exponential Smoothing Holt Double Simple Exponential Smoothing Brown Weighted Moving Average Event Model
Markdown	[![John Galt Solutions Help Center home page](https://johngalt.zendesk.com/hc/theming_assets/01JNPMK8R7H0JBZ0D9F133BC06)](https://johngalt.zendesk.com/hc/en-us "Home") [Submit a request](https://johngalt.zendesk.com/hc/en-us/requests/new) [Sign in](https://johngalt.zendesk.com/access?locale=en-us&brand_id=360001924051&return_to=https%3A%2F%2Fjohngalt.zendesk.com%2Fhc%2Fen-us%2Farticles%2F360021754152-Simple-Exponential-Smoothing&locale=en-us "Opens a dialog") 1. [John Galt Solutions](https://johngalt.zendesk.com/hc/en-us) 2. [ForecastX](https://johngalt.zendesk.com/hc/en-us/categories/360001318071-ForecastX) 3. [Forecast Methods](https://johngalt.zendesk.com/hc/en-us/sections/360003390211-Forecast-Methods) ### Articles in this section - [Overview](https://johngalt.zendesk.com/hc/en-us/articles/360021961651-Overview) - [Adaptive Exponential Smoothing](https://johngalt.zendesk.com/hc/en-us/articles/360021743312-Adaptive-Exponential-Smoothing) - [Bass Model](https://johngalt.zendesk.com/hc/en-us/articles/360021743732-Bass-Model) - [Box-Jenkins](https://johngalt.zendesk.com/hc/en-us/articles/360021744612-Box-Jenkins) - [Census X-11](https://johngalt.zendesk.com/hc/en-us/articles/360021966211-Census-X-11) - [Croston Intermittent](https://johngalt.zendesk.com/hc/en-us/articles/360021746832-Croston-Intermittent) - [Decomposition](https://johngalt.zendesk.com/hc/en-us/articles/360021967591-Decomposition) - [Double Simple Exponential Smoothing Brown](https://johngalt.zendesk.com/hc/en-us/articles/360021967611-Double-Simple-Exponential-Smoothing-Brown) - [Double Exponential Smoothing Holt](https://johngalt.zendesk.com/hc/en-us/articles/360021967791-Double-Exponential-Smoothing-Holt) - [Erlang B](https://johngalt.zendesk.com/hc/en-us/articles/360021968131-Erlang-B) [See more](https://johngalt.zendesk.com/hc/en-us/sections/360003390211-Forecast-Methods) # Simple Exponential Smoothing - August 20, 2019 22:27 - Updated [Follow](https://johngalt.zendesk.com/hc/en-us/articles/360021754152-Simple-Exponential-Smoothing/subscription.html "Opens a sign-in dialog") Simple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast. Depending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values. The level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value. To use the Simple Exponential Smoothing forecasting method: 1. Click ![](https://johngalt.zendesk.com/hc/article_attachments/360018688151/mceclip0.png) and open the ForecastingExamples.xls file. Note: The ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available. 2. Click the Simple Exp. Smoothing tab at the bottom of the Excel sheet to select the example spreadsheet. 3. Click in a cell containing data and open ForecastX™ by clicking ![](https://johngalt.zendesk.com/hc/article_attachments/360018813312/mceclip1.png). ForecastX appears. 4. Click the Forecast Method tab. 5. From the Forecast Technique menu, scroll through the list of methods and select Simple Exponential Smoothing. The Simple Exponential Smoothing Forecasting technique appears. ![simple\_exp\_smoothing.png](https://johngalt.zendesk.com/hc/article_attachments/360027475692/simple_exp_smoothing.png) 6. Enable the Edit Parameters checkbox to activate Simple Exponential Smoothing’s parameters. 7. In the Smoothing Constants section, enter the Level. 8. On the Reports tab, enable the Audit checkbox. Note: The Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast. 9. Click Finish. ForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart. One line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values. ![mceclip0.png](https://johngalt.zendesk.com/hc/article_attachments/360036623092/mceclip0.png) ForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast. ![mceclip1.png](https://johngalt.zendesk.com/hc/article_attachments/360036623172/mceclip1.png) You can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing. © 2026 John Galt Solutions. All rights reserved. Confidential do not distribute. Was this article helpful? 0 out of 0 found this helpful Have more questions? [Submit a request](https://johngalt.zendesk.com/hc/en-us/requests/new) [Return to top](https://johngalt.zendesk.com/hc/en-us/articles/360021754152-Simple-Exponential-Smoothing#article-container) ### Related articles - [Slow Moving Demand](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCKgD%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI3L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NDc5Mi1TbG93LU1vdmluZy1EZW1hbmQGOwhUOglyYW5raQY%3D--9b633b803b0feb35906196d8a6a573fd535fdcca) - [Double Exponential Smoothing Holt](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCK9D%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJGL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2Nzc5MS1Eb3VibGUtRXhwb25lbnRpYWwtU21vb3RoaW5nLUhvbHQGOwhUOglyYW5raQc%3D--20d0ee26e5234e2923418944e4965328c28504c7) - [Double Simple Exponential Smoothing Brown](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCPtC%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJOL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2NzYxMS1Eb3VibGUtU2ltcGxlLUV4cG9uZW50aWFsLVNtb290aGluZy1Ccm93bgY7CFQ6CXJhbmtpCA%3D%3D--0d0edb317064f9392cc0d76d967c2c5a3fdb3faa) - [Weighted Moving Average](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCBAK%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI8L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NjQzMi1XZWlnaHRlZC1Nb3ZpbmctQXZlcmFnZQY7CFQ6CXJhbmtpCQ%3D%3D--5f7fe6d3bc7546d1c414e758cc887051d736ef32) - [Event Model](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCNzy99JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSIwL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1MDQ5Mi1FdmVudC1Nb2RlbAY7CFQ6CXJhbmtpCg%3D%3D--199fcdca811581c20039c6a35b5d8297f7caa1e4) ### Comments 0 comments Please [sign in](https://johngalt.zendesk.com/access?locale=en-us&brand_id=360001924051&return_to=https%3A%2F%2Fjohngalt.zendesk.com%2Fhc%2Fen-us%2Farticles%2F360021754152-Simple-Exponential-Smoothing) to leave a comment. © 2026 John Galt Solutions. 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Readable Markdown	- [Overview](https://johngalt.zendesk.com/hc/en-us/articles/360021961651-Overview) - [Adaptive Exponential Smoothing](https://johngalt.zendesk.com/hc/en-us/articles/360021743312-Adaptive-Exponential-Smoothing) - [Bass Model](https://johngalt.zendesk.com/hc/en-us/articles/360021743732-Bass-Model) - [Box-Jenkins](https://johngalt.zendesk.com/hc/en-us/articles/360021744612-Box-Jenkins) - [Census X-11](https://johngalt.zendesk.com/hc/en-us/articles/360021966211-Census-X-11) - [Croston Intermittent](https://johngalt.zendesk.com/hc/en-us/articles/360021746832-Croston-Intermittent) - [Decomposition](https://johngalt.zendesk.com/hc/en-us/articles/360021967591-Decomposition) - [Double Simple Exponential Smoothing Brown](https://johngalt.zendesk.com/hc/en-us/articles/360021967611-Double-Simple-Exponential-Smoothing-Brown) - [Double Exponential Smoothing Holt](https://johngalt.zendesk.com/hc/en-us/articles/360021967791-Double-Exponential-Smoothing-Holt) - [Erlang B](https://johngalt.zendesk.com/hc/en-us/articles/360021968131-Erlang-B) [See more](https://johngalt.zendesk.com/hc/en-us/sections/360003390211-Forecast-Methods) [Follow](https://johngalt.zendesk.com/hc/en-us/articles/360021754152-Simple-Exponential-Smoothing/subscription.html "Opens a sign-in dialog") Simple Exponential Smoothing uses both historical data and fitted data to generate a new forecast. Simple Exponential Smoothing gives higher importance to recent values than older observations. For example, in a three-period Moving Average forecast, February, March and April actual values will evenly determine May's forecast. Depending on the level given to the data series, a Simple Exponential Smoothing Method creates a forecast for May that relies more so on April's observations. Simple Exponential Smoothing is most effective as a forecasting method when irregular influences comprise the main effects on the Historical time series values. The level of smoothing is always a value between zero and one. If the level value is closer to 1, then the forecasted value is going to more closely reflect the last actual value. If the value is closer to 0, then the forecasted value is going to more closely reflect the first actual value. To use the Simple Exponential Smoothing forecasting method: 1. Click ![](https://johngalt.zendesk.com/hc/article_attachments/360018688151/mceclip0.png) and open the ForecastingExamples.xls file. Note: The ForecastingExamples.xls file is a data example to demonstrate how the Simple Exponential Smoothing method is used. For your company’s purposes, you will have your own data available. 2. Click the Simple Exp. Smoothing tab at the bottom of the Excel sheet to select the example spreadsheet. 3. Click in a cell containing data and open ForecastX™ by clicking ![](https://johngalt.zendesk.com/hc/article_attachments/360018813312/mceclip1.png). ForecastX appears. 4. Click the Forecast Method tab. 5. From the Forecast Technique menu, scroll through the list of methods and select Simple Exponential Smoothing. The Simple Exponential Smoothing Forecasting technique appears. ![simple\_exp\_smoothing.png](https://johngalt.zendesk.com/hc/article_attachments/360027475692/simple_exp_smoothing.png) 6. Enable the Edit Parameters checkbox to activate Simple Exponential Smoothing’s parameters. 7. In the Smoothing Constants section, enter the Level. 8. On the Reports tab, enable the Audit checkbox. Note: The Audit Trail report provides a graph of your forecast and analyzes the statistics used to measure the Simple Exponential Smoothing forecast. 9. Click Finish. ForecastX provides two tools to judge the accuracy of your forecast and the direction your business is heading. The first thing you see is the Executive Report chart. One line represents the fitted values and the forecasted values of the actual values. The other line shows the actual values. ![mceclip0.png](https://johngalt.zendesk.com/hc/article_attachments/360036623092/mceclip0.png) ForecastX also produces a detailed table that shows the optimized parameters for your Exponential Smoothing forecast. ![mceclip1.png](https://johngalt.zendesk.com/hc/article_attachments/360036623172/mceclip1.png) You can see on the table that this forecast has an average error of 6.53%. This means that ForecastX smoothed your data to a considerable degree. A lower smoothing value represents a high level of smoothing. ### Related articles - [Slow Moving Demand](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCKgD%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI3L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NDc5Mi1TbG93LU1vdmluZy1EZW1hbmQGOwhUOglyYW5raQY%3D--9b633b803b0feb35906196d8a6a573fd535fdcca) - [Double Exponential Smoothing Holt](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCK9D%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJGL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2Nzc5MS1Eb3VibGUtRXhwb25lbnRpYWwtU21vb3RoaW5nLUhvbHQGOwhUOglyYW5raQc%3D--20d0ee26e5234e2923418944e4965328c28504c7) - [Double Simple Exponential Smoothing Brown](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCPtC%2B9JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSJOL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTk2NzYxMS1Eb3VibGUtU2ltcGxlLUV4cG9uZW50aWFsLVNtb290aGluZy1Ccm93bgY7CFQ6CXJhbmtpCA%3D%3D--0d0edb317064f9392cc0d76d967c2c5a3fdb3faa) - [Weighted Moving Average](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCBAK%2BNJTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSI8L2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1NjQzMi1XZWlnaHRlZC1Nb3ZpbmctQXZlcmFnZQY7CFQ6CXJhbmtpCQ%3D%3D--5f7fe6d3bc7546d1c414e758cc887051d736ef32) - [Event Model](https://johngalt.zendesk.com/hc/en-us/related/click?data=BAh7CjobZGVzdGluYXRpb25fYXJ0aWNsZV9pZGwrCNzy99JTADoYcmVmZXJyZXJfYXJ0aWNsZV9pZGwrCCgB%2BNJTADoLbG9jYWxlSSIKZW4tdXMGOgZFVDoIdXJsSSIwL2hjL2VuLXVzL2FydGljbGVzLzM2MDAyMTc1MDQ5Mi1FdmVudC1Nb2RlbAY7CFQ6CXJhbmtpCg%3D%3D--199fcdca811581c20039c6a35b5d8297f7caa1e4)
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