Brian Head
11/08/2023, 8:58 PMJosé Morales
11/08/2023, 9:41 PMBrian Head
11/08/2023, 9:42 PMJosé Morales
11/08/2023, 9:44 PMBrian Head
11/08/2023, 9:45 PMJosé Morales
11/08/2023, 10:44 PMMax (Nixtla)
11/09/2023, 4:30 AMBrian Head
11/09/2023, 3:30 PMintervals = ConformalIntervals(h=toggle, n_windows=5)
SF_models = [
# AutoARIMA(prediction_intervals=intervals, alias="ARIMA"), #arima works for in sample
AutoETS(prediction_intervals=intervals, season_length=12, alias="NonSeasonalAutoETS"), #ets works for in sample
AutoETS(prediction_intervals=intervals, season_length=12, model='ZZA', alias="SeasonalAutoETS"), #ets works for in sample
# AutoCES(prediction_intervals=intervals), #CES works for in sample
# AutoTheta(prediction_intervals=intervals, alias="Theta"), #Theta1 works for in sample; decomposition_type='additive',
# DynamicOptimizedTheta(prediction_intervals=intervals, season_length=13),
# Holt(prediction_intervals=intervals, season_length=13),
# HoltWinters(prediction_intervals=intervals, season_length=13)
]
José Morales
11/09/2023, 5:40 PMBrian Head
11/09/2023, 5:43 PMJosé Morales
11/09/2023, 6:07 PMBrian Head
11/09/2023, 6:12 PMJosé Morales
11/09/2023, 6:26 PMfrom statsforecast import StatsForecast
from statsforecast.models import AutoETS
from statsforecast.utils import ConformalIntervals, generate_series
series = generate_series(1, freq='M')
intervals = ConformalIntervals(h=12, n_windows=5)
sf = StatsForecast(
models=[
AutoETS(prediction_intervals=intervals, season_length=12, alias="NonSeasonalAutoETS")
],
freq='M',
)
sf.forecast(df=series, h=12)
Brian Head
11/09/2023, 6:32 PMJosé Morales
11/09/2023, 6:32 PMBrian Head
11/09/2023, 6:33 PM