Machine Learning Projects
with 5 univariate time series datasets - Chocolate Production, JetRail, Air Passengers, Wine Sales, and Portland Riders
BONUS 1) [Data Cleaning] - WIP
with 3 univariate time series datasets - JetRail (by day), Air Passengers (by month - multiplicative), and Wine Sales (by month - additive)
with 3 multivariate time series datasets for VAR models - TBD
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[Summary - All Simple Models in One Notebook Comparison (Python)] - WIP
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Simple Exponential Smoothing (SES) Forecasting Approach (Python)
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Holt Winter’s Exponential Smoothing (HWES) Forecasting Approach (Python)
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Autoregressive Integrated Moving Average (ARIMA) Approach (Python)
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[Seasonal Autoregressive Integrated Moving-Average (SARIMA) Approach (Python)] - WIP
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[Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX) Approach (Python)] - WIP
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[Vector Autoregression (VAR) Approach (Python)] - WIP
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[Vector Autoregression Moving-Average (VARMA) Approach (Python)] - WIP
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[Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX) Approach (Python)] - WIP
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[Forecast Combinations Approach (Python)] - WIP
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Seasonal Naive Approach (R) - WIP
BONUS 1) [All Simple Models with R] - WIP
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Auto-Arima (R)
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Auto-Arima (Python) with Pyramid
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LSTM Time Series with Keras
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LSTM Time Series with PyTorch
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Boosting Algorithm - Adaboost Regressor (Python) with Tsfresh
Credit to E. E. Holmes, M. D. Scheuerell, and E. J. Ward - NWFSC: Northwest Fisheries Science Centern (https://nwfsc-timeseries.github.io/atsa-labs/)