Holt's Winter Exponential Smoothing
Holts’ Winter Exponential Smoothing (HWES) or Triple Exponential Smoothing (TES) is a robust model which captures level and trend along with seasonality component.
levelt = levelt-1 + trendt-1 + (alpha) * (observedt-1 - levelt-1 - trendt-1 - seasont-m)
trendt = trendt-1 + beta *(levelt - levelt-1 - trendt-1 )
seasont = seasont-m + gamma*(observedt-1 -levelt-1 - seasont-m)
forecastt+k = levelt + trendt
As discussed in HES (https://github.com/Heisenberg0203/GETS/wiki/Holt's-Exponential-Smoothing) alpha and beta controls the smoothing of level and trend respectively while gamma is the smoothing factor for seasonality over a period of time. This requires evolving five parameters, mainly alpha, beta, gamma, period_of_seasonality and step which are substituted in the above equation to make forecast using HWES.
python ponyge.py --parameters gets/tes.txt
By default it runs on Daily Waste Generated dataset. Command to run on the custom dataset:
python ponyge.py --parameters gets/tes.txt --dataset_train path_to_traindataset --dataset_test path_to_testdataset.