README.md

In JaredChung/forecastml: A fully automated forecasting framework

forecastml

resources

https://www.quora.com/Data-Science-Can-machine-learning-be-used-for-time-series-analysis https://www.r-bloggers.com/time-series-deep-learning-forecasting-sunspots-with-keras-stateful-lstm-in-r/

1. libraries

2. Forecast

3. Dpylr
4. lubridate
5. Caret
6. H2o
7. MXnet
8. Xgboost
9. light gbm

10. catboost

11. Preprocessing

12. Boxcox Transformation

13. Fourier Transformation
14. Calender Normalisation
15. Date feature engineering

16. Principle Component features

17. Forecast Techniques

18. ETS - Exponential Smoothing (state based models)

19. auto.arima - Automatic Arima Model
20. NNetar -Single Layer Feed forward neural network
21. TBATS - ARMA, Exponential smoothinm seasonality, boxcox transformation

22. VAR - Vector Auto Regression

23. Machine Learning

24. Xgboost

25. light gbm
26. Random Forest
27. SVR
28. Linear Regression
29. Ridge Regression (L2)
30. Lasso Regression (L1)

31. Elastic Net

32. Deep Learning

33. ANN

34. LSTM

35. GRU

36. Benchmarking

37. Kaggle Competition

38. Reporting

39. Shiny Dashboard

JaredChung/forecastml documentation built on May 21, 2019, 2:31 a.m.