https://github.com/elemento24/feature-engineering-time-series-forecasting

This repo consists of all the Projects & Resources from Soledad and Kishan's course, Feature Engineering for Time Series Forecasting
https://github.com/elemento24/feature-engineering-time-series-forecasting

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This repo consists of all the Projects & Resources from Soledad and Kishan's course, Feature Engineering for Time Series Forecasting

• Host: GitHub
• URL: https://github.com/elemento24/feature-engineering-time-series-forecasting
• Owner: Elemento24
• License: other
• Created: 2023-08-14T16:45:54.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-08-17T03:30:35.000Z (over 1 year ago)
• Last Synced: 2023-08-17T04:49:03.669Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 3.2 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Feature Engineering for Time Series Forecasting - Code Repository

[](https://www.trainindata.com/p/feature-engineering-for-forecasting)

![PythonVersion](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-success)

[![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)

[![Sponsorship https://www.trainindata.com/](https://img.shields.io/badge/Powered%20By-TrainInData-orange.svg)](https://www.trainindata.com/)

Published October, 2022

Actively maintained.

## Links

- [Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting)

## Table of Contents

1. **Tabularizing time series data**

	1. Features from the target

	2. Features from exogenous variables

	3. Single step forecasting

2. **Challenges in feature engineering for time series**

	1. Train-test split

	2. Pipelines

	3. Multistep forecasting

	4. Direct forecasting

	5. Recursive forecasting

	

3. **Time series decomposition**

	1. Components of a time series: trend and seasonality

	2. Multiplicative and additive models

	3. Log transform and Box-Cox

	4. Moving averages

	5. LOWESS, STL, and multiseasonal time series decomposition

4. **Missing data imputation**

	1. Forward and backward filling

	2. Linear and spline interpolation

	3. Seasonal decomposition and interpolation

5. **Outliers**

	1. Rolling statistics for outlier detection

	2. LOWESS for outlier detection

	3. STL for outlier detection

6. **Lag features**

	1. Autoregressive processes

	2. Lag plots

	3. ACF, PACF, CCF

	4. Seasonal lags

	4. Creating lags with open-source

7. **Window features**

	1. Rolling windows

	2. Expanding windows

	3. Exponentially weighted windows

	4. Creating window features with open-source

8. **Trend features**

	1. Using time to model linear trend

    2. Polynomial features of time to model non-linear trend

	3. Changepoints & piecweise linear trends to model non-linear trend

	4. Forecasting time series with trend using tree-based models

	5. Creating trend features with open-source

9. **Seasonality features**

	1. Seasonal lags

	2. Seasonal dummies

	3. Seasonal decomposition methods

	4. Fourier terms

	5. Creating seasonality features with open-source

10. **Datetime features**

	1. Extracting features from date and time

	2. Periodic features

	3. Calendar events

	4. Creating datetime features with open-source

11. **Categorical Features**

	1. One hot encoding

	2. Target encoding

	3. Rolling entropy and rolling majority

- [Online Course](https://www.trainindata.com/p/feature-engineering-for-forecasting)