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Apress Source Code

This repository accompanies Hands-on Time Series Analysis with Python by B V Vishwas and Ashish Patel (Apress, 2020).

Cover image

Download the files as a zip using the green button, or clone the repository to your machine using Git.

Installation

 pip install -r requirements.txt

Chapter-1: Time-Series Characteristics

Topic Notebook Colab
1.Trend Github
2.Detrending using Differencing Github
3.Detrending using Scipy Signal Github
4.Detrending using HP Filter Github
5.Multi Month-wise Box Plot Github
6.Autocorrelation plot for seasonality Github
7.Deseasoning Time series Github
8.Detecting cyclical variation Github
9.Decompose Time series Github

Chapter-2: Data Wrangling and Preparation for Time Series

Topic Notebook Colab
Data wrangling using pandas and pandasql Github

Chapter-3: Smoothing Methods

Topic Notebook Colab
1. Simple exponential smoothing Github
2. Double Exponential Smoothing Github
3. Triple Exponential Smoothing Github

Chapter-4: Regression Extension Techniques for Time- Series Data

Topic Notebook Colab
1. AR and MA Github
2. Stationary Github
3. ARIMA Github
4. SARIMA Github
5. SARIMAX Github
6. VAR Github
7. VARMA with Auto Arima Github
8. VARMA with Gird Search Github

Chapter-5: Bleeding-Edge Techniques

This chapter contains deep learning theory.

Chapter-6: Bleeding-Edge Techniques for Univariate Time Series

Topic Notebook Colab
1. Bidirectional LSTM Univarient Single Step Style Github
2. Bidirectional LSTM Univarient Horizon Style Github
3. CNN Univarient Horizon Style Github
4. CNN Univarient Single Step Style Github
5. Encoder Decoder LSTM Univariate Horizon Style Github
6. Encoder Decoder LSTM Univarient Single Step Style Github
7. GRU Univarient Single Step Style Github
8. GRU Univarient Horizon Style Github
9. LSTM Univariate Horizon Style Github
10. LSTM Univarient Single Step Style Github

Chapter-7: Bleeding-Edge Techniques for Multivariate Time Series

Topic Notebook Colab
1. Bidirectional LSTM Multivariate Horizon Style Github
2. CNN Multivariate Horizon Style Github
3. Encoder Decoder LSTM Multivariate Horizon Style Github
4. GRU Multivariate Horizon Style Github
5. LSTM Multivariate Horizon Style Github

Chapter-8 : Prophet

Topic Notebook Colab
1. fbprophet Github
2. fbprophet with log transformation Github
3. fbprophet adding country holiday Github
4. fbprophet with exogenous or add_regressors Github

Note: All Jupyter Notebook Sample Data is available in Data Folder

Releases

Release v1.0 corresponds to the code in the published book, without corrections or updates.

Contributions

See the file Contributing.md for more information on how you can contribute to this repository.