🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 77 (from laksa135)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa077.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://medium.com/@m.avinash/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39\')), getAhrefsUnparsedNoserviceFromURL(\'https://medium.com/@m.avinash/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/medium.com\/@m.avinash\/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39","crawl_time":1766841315,"first_indexed_time":0,"http_code":200,"src_unparsed":"com,medium!\/@m.avinash\/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39 s443","src_root_hash":"13179037029838926277","history_drop_reason":null,"meta_title":"Simple Exponential Smoothing Explained – From Intuition to Formula | by Avinash Mohan | Dec, 2025 | Medium","meta_descriptions":["Simple Exponential Smoothing Explained – From Intuition to Formula Forecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing …"],"attrs_boilerpipe_text":"Forecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing with time series data that fluctuates around a stable level, one of the most widely used techniques is  Simple Exponential Smoothing (SES) . This article explains SES from first principles —  why it is needed, how it works, and when it should (and should not) be used . 1. The Forecasting Problem Suppose we observe a time series: and want to forecast the next value ( Y_{t+1} ). A forecasting method must: Capture the underlying pattern (signal) Reduce random fluctuations (noise) Adapt to changes in the data Be computationally efficient Early forecasting approaches struggle to satisfy all of these simultaneously. 2. Why Simple Averages Are Not Enough Mean as a Forecast The simplest forecasting method uses the  mean of all past observations  as the forecast: This approach assumes: No trend No seasonality A stable, constant process","attrs_markdown":"[Sitemap](https:\/\/medium.com\/sitemap\/sitemap.xml)\n\n[Open in app](https:\/\/play.google.com\/store\/apps\/details?id=com.medium.reader&referrer=utm_source%3DmobileNavBar&source=post_page---top_nav_layout_nav-----------------------------------------)\n\nSign up\n\n[Sign in](https:\/\/medium.com\/m\/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40m.avinash%2Fsimple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39&source=post_page---top_nav_layout_nav-----------------------global_nav------------------)\n\n[Medium Logo](https:\/\/medium.com\/?source=post_page---top_nav_layout_nav-----------------------------------------)\n\n[Write](https:\/\/medium.com\/m\/signin?operation=register&redirect=https%3A%2F%2Fmedium.com%2Fnew-story&source=---top_nav_layout_nav-----------------------new_post_topnav------------------)\n\n[Search](https:\/\/medium.com\/search?source=post_page---top_nav_layout_nav-----------------------------------------)\n\nSign up\n\n[Sign in](https:\/\/medium.com\/m\/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40m.avinash%2Fsimple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39&source=post_page---top_nav_layout_nav-----------------------global_nav------------------)\n\n![](https:\/\/miro.medium.com\/v2\/resize:fill:64:64\/1*dmbNkD5D-u45r44go_cf0g.png)\n\nMember-only story\n\n# Simple Exponential Smoothing Explained – From Intuition to Formula\n[![Avinash Mohan](https:\/\/miro.medium.com\/v2\/resize:fill:64:64\/1*dmbNkD5D-u45r44go_cf0g.png)](https:\/\/medium.com\/@m.avinash?source=post_page---byline--8acb4415bb39---------------------------------------)\n\n[Avinash Mohan](https:\/\/medium.com\/@m.avinash?source=post_page---byline--8acb4415bb39---------------------------------------)\n\n4 min read\n\n·\n\nDec 15, 2025\n\n\\--\n\nShare\n\nForecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing with time series data that fluctuates around a stable level, one of the most widely used techniques is **Simple Exponential Smoothing (SES)**.\n\nThis article explains SES from first principles — *why it is needed, how it works, and when it should (and should not) be used*.\n\n## 1\\. The Forecasting Problem\nSuppose we observe a time series:\n\nand want to forecast the next value ( Y\\_{t+1} ).\n\nA forecasting method must:\n\n- Capture the underlying pattern (signal)\n- Reduce random fluctuations (noise)\n- Adapt to changes in the data\n- Be computationally efficient\n\nEarly forecasting approaches struggle to satisfy all of these simultaneously.\n\n## 2\\. Why Simple Averages Are Not Enough\n## Mean as a Forecast\nThe simplest forecasting method uses the **mean of all past observations** as the forecast:\n\nThis approach assumes:\n\n- No trend\n- No seasonality\n- A stable, constant process\n\n\\--\n\n\\--\n\n[![Avinash Mohan](https:\/\/miro.medium.com\/v2\/resize:fill:96:96\/1*dmbNkD5D-u45r44go_cf0g.png)](https:\/\/medium.com\/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------)\n\n[![Avinash Mohan](https:\/\/miro.medium.com\/v2\/resize:fill:128:128\/1*dmbNkD5D-u45r44go_cf0g.png)](https:\/\/medium.com\/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------)\n\n[Written by Avinash Mohan](https:\/\/medium.com\/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------)\n\n[2 followers](https:\/\/medium.com\/@m.avinash\/followers?source=post_page---post_author_info--8acb4415bb39---------------------------------------)\n\n·[1 following](https:\/\/medium.com\/@m.avinash\/following?source=post_page---post_author_info--8acb4415bb39---------------------------------------)\n\nSoftware Architect with over a decade of engineering experience.\n## No responses yet\n[Help](https:\/\/help.medium.com\/hc\/en-us?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Status](https:\/\/status.medium.com\/?source=post_page-----8acb4415bb39---------------------------------------)\n\n[About](https:\/\/medium.com\/about?autoplay=1&source=post_page-----8acb4415bb39---------------------------------------)\n\n[Careers](https:\/\/medium.com\/jobs-at-medium\/work-at-medium-959d1a85284e?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Press](mailto:pressinquiries@medium.com)\n\n[Blog](https:\/\/blog.medium.com\/?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Privacy](https:\/\/policy.medium.com\/medium-privacy-policy-f03bf92035c9?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Rules](https:\/\/policy.medium.com\/medium-rules-30e5502c4eb4?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Terms](https:\/\/policy.medium.com\/medium-terms-of-service-9db0094a1e0f?source=post_page-----8acb4415bb39---------------------------------------)\n\n[Text to speech](https:\/\/speechify.com\/medium?source=post_page-----8acb4415bb39---------------------------------------)","attrs_readable_markdown":null,"meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

3 months ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	3.8 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://medium.com/@m.avinash/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39
Last Crawled	2025-12-27 13:15:15 (3 months ago)
First Indexed	not set
HTTP Status Code	200
Meta Title	Simple Exponential Smoothing Explained – From Intuition to Formula \| by Avinash Mohan \| Dec, 2025 \| Medium
Meta Description	Simple Exponential Smoothing Explained – From Intuition to Formula Forecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing …
Meta Canonical	null
Boilerpipe Text	Forecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing with time series data that fluctuates around a stable level, one of the most widely used techniques is Simple Exponential Smoothing (SES) . This article explains SES from first principles — why it is needed, how it works, and when it should (and should not) be used . 1. The Forecasting Problem Suppose we observe a time series: and want to forecast the next value ( Y_{t+1} ). A forecasting method must: Capture the underlying pattern (signal) Reduce random fluctuations (noise) Adapt to changes in the data Be computationally efficient Early forecasting approaches struggle to satisfy all of these simultaneously. 2. Why Simple Averages Are Not Enough Mean as a Forecast The simplest forecasting method uses the mean of all past observations as the forecast: This approach assumes: No trend No seasonality A stable, constant process
Markdown	[Sitemap](https://medium.com/sitemap/sitemap.xml) [Open in app](https://play.google.com/store/apps/details?id=com.medium.reader&referrer=utm_source%3DmobileNavBar&source=post_page---top_nav_layout_nav-----------------------------------------) Sign up [Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40m.avinash%2Fsimple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39&source=post_page---top_nav_layout_nav-----------------------global_nav------------------) [Medium Logo](https://medium.com/?source=post_page---top_nav_layout_nav-----------------------------------------) [Write](https://medium.com/m/signin?operation=register&redirect=https%3A%2F%2Fmedium.com%2Fnew-story&source=---top_nav_layout_nav-----------------------new_post_topnav------------------) [Search](https://medium.com/search?source=post_page---top_nav_layout_nav-----------------------------------------) Sign up [Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40m.avinash%2Fsimple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39&source=post_page---top_nav_layout_nav-----------------------global_nav------------------) ![](https://miro.medium.com/v2/resize:fill:64:64/1dmbNkD5D-u45r44go_cf0g.png) Member-only story # Simple Exponential Smoothing Explained – From Intuition to Formula [![Avinash Mohan](https://miro.medium.com/v2/resize:fill:64:64/1dmbNkD5D-u45r44go_cf0g.png)](https://medium.com/@m.avinash?source=post_page---byline--8acb4415bb39---------------------------------------) [Avinash Mohan](https://medium.com/@m.avinash?source=post_page---byline--8acb4415bb39---------------------------------------) 4 min read · Dec 15, 2025 \-- Share Forecasting future values from historical data is a core problem in statistics, economics, and business analytics. When dealing with time series data that fluctuates around a stable level, one of the most widely used techniques is Simple Exponential Smoothing (SES). This article explains SES from first principles — why it is needed, how it works, and when it should (and should not) be used. ## 1\. The Forecasting Problem Suppose we observe a time series: and want to forecast the next value ( Y\_{t+1} ). A forecasting method must: - Capture the underlying pattern (signal) - Reduce random fluctuations (noise) - Adapt to changes in the data - Be computationally efficient Early forecasting approaches struggle to satisfy all of these simultaneously. ## 2\. Why Simple Averages Are Not Enough ## Mean as a Forecast The simplest forecasting method uses the mean of all past observations as the forecast: This approach assumes: - No trend - No seasonality - A stable, constant process \-- \-- [![Avinash Mohan](https://miro.medium.com/v2/resize:fill:96:96/1dmbNkD5D-u45r44go_cf0g.png)](https://medium.com/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------) [![Avinash Mohan](https://miro.medium.com/v2/resize:fill:128:128/1dmbNkD5D-u45r44go_cf0g.png)](https://medium.com/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------) [Written by Avinash Mohan](https://medium.com/@m.avinash?source=post_page---post_author_info--8acb4415bb39---------------------------------------) [2 followers](https://medium.com/@m.avinash/followers?source=post_page---post_author_info--8acb4415bb39---------------------------------------) ·[1 following](https://medium.com/@m.avinash/following?source=post_page---post_author_info--8acb4415bb39---------------------------------------) Software Architect with over a decade of engineering experience. ## No responses yet [Help](https://help.medium.com/hc/en-us?source=post_page-----8acb4415bb39---------------------------------------) [Status](https://status.medium.com/?source=post_page-----8acb4415bb39---------------------------------------) [About](https://medium.com/about?autoplay=1&source=post_page-----8acb4415bb39---------------------------------------) [Careers](https://medium.com/jobs-at-medium/work-at-medium-959d1a85284e?source=post_page-----8acb4415bb39---------------------------------------) [Press](mailto:pressinquiries@medium.com) [Blog](https://blog.medium.com/?source=post_page-----8acb4415bb39---------------------------------------) [Privacy](https://policy.medium.com/medium-privacy-policy-f03bf92035c9?source=post_page-----8acb4415bb39---------------------------------------) [Rules](https://policy.medium.com/medium-rules-30e5502c4eb4?source=post_page-----8acb4415bb39---------------------------------------) [Terms](https://policy.medium.com/medium-terms-of-service-9db0094a1e0f?source=post_page-----8acb4415bb39---------------------------------------) [Text to speech](https://speechify.com/medium?source=post_page-----8acb4415bb39---------------------------------------)
Readable Markdown	null
Shard	77 (laksa)
Root Hash	13179037029838926277
Unparsed URL	com,medium!/@m.avinash/simple-exponential-smoothing-from-intuition-to-formula-8acb4415bb39 s443