def createHoltWintersModel(series, nextPredictHours=1, threshold=2, slen=1):
y = series.y
x = [0, 0, 0]
# Minimizing the loss function
ret = None
alpha_final, beta_final, gamma_final = 0.1, 0.002, 0.06
try:
ret = minimize(tsCrossValidationScore,
x0=x,
args=(y, mean_squared_log_error),
method="TNC",
bounds=((0, 1), (0, 1), (0, 1)))
alpha_final, beta_final, gamma_final = ret.x
except Exception as e:
logging.error("Data is not seasonality model", e)
model = HoltWinters(y,
slen=slen,
alpha=alpha_final,
beta=beta_final,
gamma=gamma_final,
n_preds=nextPredictHours,
scaling_factor=threshold)
model.triple_exponential_smoothing()
return model
def tsCrossValidationScore(params, series,loss_function=mean_squared_error, nsplits=3, slen=1):
"""
#Parameters:
params : vector of parameters for optimization (three parameters:
alpha, beta, gamma for example
series : dataset with timeseries
sle:
Returns:
error on CrossValidation
"""
# errors array
errors = []
values = series.values
alpha, beta, gamma = params
# set the number of folds for cross-validation
tscv = TimeSeriesSplit(n_splits=nsplits)
# iterating over folds, train model on each, forecast and calculate error
for train, test in tscv.split(values):
model = HoltWinters(series=values[train], slen=slen,
alpha=alpha, beta=beta, gamma=gamma, n_preds=len(test))
model.triple_exponential_smoothing()
predictions = model.result[-len(test):]
actual = values[test]
error = loss_function(predictions, actual)
errors.append(error)
return np.mean(np.array(errors))