def driftmethod(df, week, week_to_forecast=27):
for i in range(0, week_to_forecast):
y_t = df.loc[df.index[len(df) - 1]]['vendite']
m = (y_t - df.loc[df.index[0]]['vendite']) / len(df)
h = 1
val_forecast = y_t + m * h
week = add_week(week, 1)
df.loc[week] = val_forecast
return df
def seasonal_naive_forecasting(df,
last_week,
season_length,
h,
week_to_forecast=27):
k = int((h - 1) / season_length)
for i in range(0, week_to_forecast):
last_week = add_week(last_week, 1)
df.loc[last_week] = df.iloc[df.size + h - season_length * (k + 1)]
return df
def smpExpSmoth(df, num_of_forecast):
dateiso = []
for week in df.index:
dateiso.append(dateutil.parser.isoparse(week))
dateiso = pd.DatetimeIndex(dateiso).to_period('W')
series = pd.Series(data=df['vendite'].values, index=dateiso)
model = SimpleExpSmoothing(series, initialization_method='estimated').fit(
smoothing_level=0.6, optimized=True)
predict = model.forecast(num_of_forecast)
week = df.index[df.index.size - 1]
for i in range(0, num_of_forecast):
week = add_week(week, 1)
df.loc[week] = predict.iloc[i]
return df
def sarima_forecast(df,
config,
weektopredict=1,
decomposition=False,
box_cox=0,
rmv_outliers=True):
dateiso = []
for week in df.index:
dateiso.append(dateutil.parser.isoparse(week))
dateiso = pd.DatetimeIndex(dateiso).to_period('W')
df_o = df.copy()
if rmv_outliers:
df_o = remove_outliers(df)
if box_cox != 0:
df_o = box_cox_transformation(df_o.copy(), box_cox)
series = pd.Series(data=df_o['vendite'].values, index=dateiso)
config = list(config)
order = config[0]
sorder = config[1]
trend = config[2]
if decomposition:
model = STLForecast(series,
SARIMAX,
period=26,
model_kwargs=dict(order=order,
seasonal_order=sorder,
trend=trend))
model_fit = model.fit(fit_kwargs=dict(disp=False))
else:
model = SARIMAX(series,
order=order,
seasonal_order=sorder,
trend=trend)
model_fit = model.fit(disp=False)
if box_cox != 0:
predict = box_cox_transformation(model_fit.forecast(weektopredict),
box_cox,
reverse=True)
else:
predict = model_fit.forecast(weektopredict)
week = df.index[df.index.size - 1]
for i in range(0, weektopredict):
week = add_week(week, 1)
df.loc[week] = predict.iloc[i]
return df
def seasonalExp_smoothing(df,
week_to_predict=1,
decompositon=False,
box_cox=0,
rmv_outliers=True):
dateiso = []
for week in df.index:
dateiso.append(dateutil.parser.isoparse(week))
dateiso = pd.DatetimeIndex(dateiso).to_period('W')
df_o = df.copy()
if rmv_outliers:
df_o = remove_outliers(df)
if box_cox != 0:
df_o = box_cox_transformation(df_o.copy(), box_cox)
series = pd.Series(data=df_o['vendite'].values, index=dateiso)
if decompositon:
model = STLForecast(series,
ExponentialSmoothing,
period=26,
model_kwargs=dict(
seasonal_periods=26,
seasonal='add',
initialization_method="estimated"))
model_fitted = model.fit()
else:
model = ExponentialSmoothing(series,
seasonal_periods=26,
seasonal='add',
initialization_method='estimated')
model_fitted = model.fit()
if box_cox != 0:
predict = box_cox_transformation(
model_fitted.forecast(week_to_predict), box_cox, reverse=True)
else:
predict = model_fitted.forecast(week_to_predict)
week = df.index[df.index.size - 1]
for i in range(0, week_to_predict):
week = add_week(week, 1)
df.loc[week] = predict.iloc[i]
return df
def average_forecasting(df, last_week, week_to_forecast=27):
for i in range(0, week_to_forecast):
last_week = add_week(last_week, 1)
df.loc[last_week] = int(df.mean())
return df
def naive(df, week, week_to_forecast=27):
for i in range(0, week_to_forecast):
week = add_week(week, 1)
df.loc[week] = df.tail(1).values[0]
return df