This repository accompanies Hands-on Time Series Analysis with Python by B V Vishwas and Ashish Patel (Apress, 2020).
Download the files as a zip using the green button, or clone the repository to your machine using Git.
pip install -r requirements.txt | 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 | |
| Topic | Notebook | Colab |
|---|---|---|
| Data wrangling using pandas and pandasql | Github | |
| Topic | Notebook | Colab |
|---|---|---|
| 1. Simple exponential smoothing | Github | |
| 2. Double Exponential Smoothing | Github | |
| 3. Triple Exponential Smoothing | Github | |
| 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 | |
This chapter contains deep learning theory.
| 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 | |
| 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 | |
| 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
Release v1.0 corresponds to the code in the published book, without corrections or updates.
See the file Contributing.md for more information on how you can contribute to this repository.