Holt-Winters Exponential Smoothing

This notebook demonstrates running an HWES model in scalecast.

• See the documentation.

import pandas as pd
import numpy as np
import pandas_datareader as pdr
import seaborn as sns
import matplotlib
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
from scalecast.Forecaster import Forecaster

Download data from FRED (https://fred.stlouisfed.org/series/HOUSTNSA). This data is interesting due to its strong seasonality and irregular cycles. It measures monthly housing starts in the USA since 1959. Since exponential smoothing generally doesn’t need as much data to work effectively, we start the series in 2010, where it has a clear trend and seasonality.

df = pdr.get_data_fred('HOUSTNSA',start='1959-01-01',end='2022-12-31')
f = Forecaster(
    y=df['HOUSTNSA'],
    current_dates=df.index,
    future_dates = 24,
    test_length = 24,
    cis = True,
)
f

Forecaster(
    DateStartActuals=1959-01-01T00:00:00.000000000
    DateEndActuals=2022-12-01T00:00:00.000000000
    Freq=MS
    N_actuals=768
    ForecastLength=24
    Xvars=[]
    TestLength=24
    ValidationMetric=rmse
    ForecastsEvaluated=[]
    CILevel=0.95
    CurrentEstimator=mlr
    GridsFile=Grids
)

f.plot()
plt.title('Housing Starts - Original',size=20)
plt.show()

Forecast

hwes_grid = {
    'trend':['add','mul',None],
    'seasonal':['add','mul',None],
    'use_boxcox':[True,False],
}

f.set_estimator('hwes')
f.ingest_grid(hwes_grid)
f.cross_validate(k=10) # 10-fold cv to find optimal hyperparams
f.auto_forecast()
f.best_params

{'trend': None, 'seasonal': 'add', 'use_boxcox': False}

f.plot_test_set(ci=True,include_train=False);

Model Summary

f.regr.summary()

ExponentialSmoothing Model Results

Dep. Variable:	HOUSTNSA	No. Observations:	768
Model:	ExponentialSmoothing	SSE	91014.035
Optimized:	True	AIC	3695.184
Trend:	None	BIC	3760.197
Seasonal:	Additive	AICC	3695.908
Seasonal Periods:	12	Date:	Mon, 10 Apr 2023
Box-Cox:	False	Time:	20:10:11
Box-Cox Coeff.:	None

	coeff	code	optimized
smoothing_level	0.6667527	alpha	True
smoothing_seasonal	0.1106022	gamma	True
initial_level	137.88929	l.0	True
initial_seasons.0	-42.836737	s.0	True
initial_seasons.1	-36.976933	s.1	True
initial_seasons.2	-3.3594145	s.2	True
initial_seasons.3	22.918534	s.3	True
initial_seasons.4	30.817362	s.4	True
initial_seasons.5	26.588908	s.5	True
initial_seasons.6	20.272506	s.6	True
initial_seasons.7	21.220218	s.7	True
initial_seasons.8	8.3880755	s.8	True
initial_seasons.9	11.692122	s.9	True
initial_seasons.10	-12.917838	s.10	True
initial_seasons.11	-36.760941	s.11	True

Save Summary Stats

f.save_summary_stats()
f.export_summary_stats('hwes')

	coeff	code	optimized
smoothing_level	0.666753	alpha	True
smoothing_seasonal	0.110602	gamma	True
initial_level	137.889290	l.0	True
initial_seasons.0	-42.836737	s.0	True
initial_seasons.1	-36.976933	s.1	True
initial_seasons.2	-3.359415	s.2	True
initial_seasons.3	22.918534	s.3	True
initial_seasons.4	30.817362	s.4	True
initial_seasons.5	26.588908	s.5	True
initial_seasons.6	20.272506	s.6	True
initial_seasons.7	21.220218	s.7	True
initial_seasons.8	8.388075	s.8	True
initial_seasons.9	11.692122	s.9	True
initial_seasons.10	-12.917838	s.10	True
initial_seasons.11	-36.760941	s.11	True