def forecast(data, train_hours, test_hours):
# Train on the first 6 days to predict the 7th day
train = data.iloc[25:train_hours]
# Create the SES Model
model = Holt(np.asarray(train['seasonally differenced']), damped=True)
model._index = pd.to_datetime(train.index)
# Fit the model, and forecast
fit = model.fit()
pred = fit.forecast(test_hours)
data['holtDamped'] = 0
data['holtDamped'][25:] = list(fit.fittedvalues) + list(pred)
def forecast(data, train_hours, test_hours):
# Split data into training and test data
train = data.iloc[25:train_hours]
# Create the Holt Model
model = Holt(np.asarray(train['seasonally differenced']))
model._index = pd.to_datetime(train.index)
# Fit the model, and forecast
fit = model.fit()
pred = fit.forecast(test_hours)
data['holt'] = 0
data['holt'][25:] = list(fit.fittedvalues) + list(pred)