Machine-Deep-Learning-in-Python-and-R/Machine-Learning-in-Python/Time-Series-Forecast at master · dundee2002/Machine-Deep-Learning-in-Python-and-R

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Name		Name	Last commit message	Last commit date
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1_data_loading_preprocessing.ipynb		1_data_loading_preprocessing.ipynb
2_persistence_modelling.ipynb		2_persistence_modelling.ipynb
3_arima_modelling.ipynb		3_arima_modelling.ipynb
4_parameter_tuning.ipynb		4_parameter_tuning.ipynb
TimeSeries_datamarket_airline_passenger_data.ipynb		TimeSeries_datamarket_airline_passenger_data.ipynb
TimeSeries_kaggle_google_stock_price_data.ipynb		TimeSeries_kaggle_google_stock_price_data.ipynb
TimeSeries_milk_production_data.ipynb		TimeSeries_milk_production_data.ipynb
readme		readme

readme

End-to-end ML modelling process for timeseries forecast
  1. Data loading and preprocessing: decomposition, autocorrelation, stationarity check, differencing
  2. Persistence modelling (baseline)
  3. Autoregressive Integrated Moving Average (ARIMA) modelling
  4. Hyperparameter tuning
  
Examples
  1. Datamarket Milk Production Data
  2. Datamarket International Airline Passenger Data
  3. kaggle Google Stock Price Data

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