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.