def get_trend(tr_dict, return_fitted=False):
"""
method of trend calculation, which fit holt
which returns also g_trend (i.e. the distance between y_holt - y_trajectory)
:param tr_dict:
:param return_fitted:
:return:
"""
trend_dict = OrderedDict()
res_dict = None
if return_fitted:
res_dict = OrderedDict()
for patient, values in tr_dict.items():
fitted = Holt(values, damped_trend=True).fit(optimized=True, smoothing_level=0.7) # type: HoltWintersResults
trend_dict[patient] = fitted.predict(start=0, end=len(values)-1)
if return_fitted:
res_dict[patient] = fitted.summary()
return trend_dict, res_dict
### Simple Exponential Method
ses_model = SimpleExpSmoothing(Train["Sales"]).fit(smoothing_level=0.9)
pred_ses = ses_model.predict(start=Test.index[0], end=Test.index[-1])
MAPE(pred_ses, Test.Sales)
### Holt method
# Holt method
hw_model = Holt(Train["Sales"]).fit(smoothing_level=0.8, smoothing_trend=0.2)
pred_hw = hw_model.predict(start=Test.index[0], end=Test.index[-1])
MAPE(pred_hw, Test.Sales)
hw_model.summary()
### Holts winter exponential smoothing with additive seasonality and additive trend
hwe_model_add_add = ExponentialSmoothing(
Train["Sales"], seasonal="add", trend="add",
seasonal_periods=4).fit() #add the trend to the model
pred_hwe_add_add = hwe_model_add_add.predict(start=Test.index[0],
end=Test.index[-1])
MAPE(pred_hwe_add_add, Test.Sales)
hwe_model_add_add.summary()
### Holts winter exponential smoothing with multiplicative seasonality and additive trend
hwe_model_mul_add = ExponentialSmoothing(Train["Sales"],
seasonal="mul",
trend="add",
seasonal_periods=4).fit()
#forecasting/ predicting next 19 periods
births_pred = births_ses.forecast(steps=19)
print(births_pred)
#Plot actual and forecast
plt.plot(births)
plt.plot(births_pred)
plt.legend(['Actual', 'Forecast - SimpleExpSmoothing'],
bbox_to_anchor=(1, 1),
loc=2)
plt.show()
#Model with double exponential smoothing
from statsmodels.tsa.holtwinters import Holt
births_holt = Holt(births).fit()
births_holt.summary()
''' Holt Model Results
==============================================================================
Dep. Variable: births No. Observations: 168
Model: Holt SSE 280.134
Optimized: True AIC 93.899
Trend: Additive BIC 106.395
Seasonal: None AICC 94.421
Seasonal Periods: None Date: Fri, 26 Mar 2021
Box-Cox: False Time: 00:23:58
Box-Cox Coeff.: None
==============================================================================
coeff code optimized
------------------------------------------------------------------------------
smoothing_level 0.4354824 alpha True
smoothing_trend 1.8914e-12 beta True