datageekette / Rossmann_tsa_forecasts
Time Series Analysis & Forecasting of Rossmann Sales with Python. EDA, TSA and seasonal decomposition, Forecasting with Prophet and XGboost modeling for regression.
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rossmann_TSA_forecasts
This project is built using the data from Rossmann competition hosted at Kaggle and then published for comfortable reading as the Jupyter notebook.
To check out the project open an .ipynb file.
Time Series Analysis & Forecasting
- Exploratory Data Analysis with Python (ECDF, missing values, Correlation analysis ...)
- Time Series Analysis per store type (Seasonal decomposition, Autocorrelation)
- Forecasting with Prophet
- Predictive modeling with XGboost
Libraries used: numpy, pandas, matplotlib, seaborn, statsmodel, fbprophet (Facebook), xgboost, sklearn.
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