{"id":13665594,"url":"https://github.com/trainindata/feature-engineering-for-time-series-forecasting","last_synced_at":"2026-01-17T10:42:01.272Z","repository":{"id":101391982,"uuid":"333085820","full_name":"trainindata/feature-engineering-for-time-series-forecasting","owner":"trainindata","description":"Code repository for the online course \"Feature Engineering for Time Series Forecasting\".","archived":false,"fork":false,"pushed_at":"2023-12-06T10:10:33.000Z","size":26089,"stargazers_count":184,"open_issues_count":2,"forks_count":131,"subscribers_count":11,"default_branch":"main","last_synced_at":"2025-04-26T08:35:41.138Z","etag":null,"topics":["forecasting","forecasting-time-series","machine-learning","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"https://www.trainindata.com/p/feature-engineering-for-forecasting","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/trainindata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-01-26T12:59:07.000Z","updated_at":"2025-04-03T15:04:53.000Z","dependencies_parsed_at":"2023-12-06T11:37:13.323Z","dependency_job_id":null,"html_url":"https://github.com/trainindata/feature-engineering-for-time-series-forecasting","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/trainindata/feature-engineering-for-time-series-forecasting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trainindata%2Ffeature-engineering-for-time-series-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trainindata%2Ffeature-engineering-for-time-series-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trainindata%2Ffeature-engineering-for-time-series-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trainindata%2Ffeature-engineering-for-time-series-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/trainindata","download_url":"https://codeload.github.com/trainindata/feature-engineering-for-time-series-forecasting/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trainindata%2Ffeature-engineering-for-time-series-forecasting/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28506593,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T10:25:30.148Z","status":"ssl_error","status_checked_at":"2026-01-17T10:25:29.718Z","response_time":85,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["forecasting","forecasting-time-series","machine-learning","time-series","time-series-analysis"],"created_at":"2024-08-02T06:00:43.371Z","updated_at":"2026-01-17T10:42:01.228Z","avatar_url":"https://github.com/trainindata.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"## Feature Engineering for Time Series Forecasting - Code Repository\n\n[\u003cimg src=\"images/FETSF_banner.png\" width=\"1500\"\u003e](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n\n\n![PythonVersion](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-success)\n[![License https://github.com/trainindata/feature-engineering-for-time-series-forecasting/blob/master/LICENSE](https://img.shields.io/badge/license-BSD-success.svg)](https://github.com/trainindata/feature-engineering-for-time-series-forecasting/blob/master/LICENSE)\n[![Sponsorship https://www.trainindata.com/](https://img.shields.io/badge/Powered%20By-TrainInData-orange.svg)](https://www.trainindata.com/)\n\nPublished October, 2022\n\nActively maintained.\n\n## Links\n\n- [Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n\n\n## Table of Contents\n\n1. **Tabularizing time series data**\n\t1. Features from the target\n\t2. Features from exogenous variables\n\t3. Single step forecasting\n\n2. **Challenges in feature engineering for time series**\n\t1. Train-test split\n\t2. Pipelines\n\t3. Multistep forecasting\n\t4. Direct forecasting\n\t5. Recursive forecasting\n\t\n3. **Time series decomposition**\n\t1. Components of a time series: trend and seasonality\n\t2. Multiplicative and additive models\n\t3. Log transform and Box-Cox\n\t4. Moving averages\n\t5. LOWESS, STL, and multiseasonal time series decomposition\n\n4. **Missing data imputation**\n\t1. Forward and backward filling\n\t2. Linear and spline interpolation\n\t3. Seasonal decomposition and interpolation\n\n5. **Outliers**\n\t1. Rolling statistics for outlier detection\n\t2. LOWESS for outlier detection\n\t3. STL for outlier detection\n\n6. **Lag features**\n\t1. Autoregressive processes\n\t2. Lag plots\n\t3. ACF, PACF, CCF\n\t4. Seasonal lags\n\t4. Creating lags with open-source\n\n7. **Window features**\n\t1. Rolling windows\n\t2. Expanding windows\n\t3. Exponentially weighted windows\n\t4. Creating window features with open-source\n\n8. **Trend features**\n\t1. Using time to model linear trend\n    2. Polynomial features of time to model non-linear trend\n\t3. Changepoints \u0026 piecweise linear trends to model non-linear trend\n\t4. Forecasting time series with trend using tree-based models\n\t5. Creating trend features with open-source\n\n9. **Seasonality features**\n\t1. Seasonal lags\n\t2. Seasonal dummies\n\t3. Seasonal decomposition methods\n\t4. Fourier terms\n\t5. Creating seasonality features with open-source\n\n10. **Datetime features**\n\t1. Extracting features from date and time\n\t2. Periodic features\n\t3. Calendar events\n\t4. Creating datetime features with open-source\n\n11. **Categorical Features**\n\t1. One hot encoding\n\t2. Target encoding\n\t3. Rolling entropy and rolling majority\n\n\n- [Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrainindata%2Ffeature-engineering-for-time-series-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftrainindata%2Ffeature-engineering-for-time-series-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrainindata%2Ffeature-engineering-for-time-series-forecasting/lists"}