def test_simple_exponential_smoothing_key(self):
ses = simple_exponential_smoothing_forecast(demand=self._orders,
alpha=0.5,
forecast_length=6,
initial_period=18)
for k in ses:
self.assertIn(k, self.ses_components)
def createSupplyChainAnalysis(pdesc):
raw_df = createPdForAnalysis()
orders_df = raw_df
analyse_kv = dict(df=raw_df,
start=1,
interval_length=12,
interval_type='months',
z_value=Decimal(1.28),
reorder_cost=Decimal(400),
retail_price=Decimal(455),
file_type='csv',
currency='USD')
print(raw_df)
analysis_df = analyse(**analyse_kv)
analysis_rev = analysis_df[['sku', 'revenue']]
row_ds = raw_df[raw_df['Sku'] == pdesc].squeeze()
ses = simple_exponential_smoothing_forecast(demand=list(row_ds[1:12]),
alpha=0.5,
forecast_length=6,
initial_period=18)
forecast_breakdown_df = pd.DataFrame(
ses.get('forecast_breakdown', 'UNKNOWN'))
regression = {
'regression': [(ses.get('statistics')['slope'] * i) +
ses.get('statistics')['intercept']
for i in range(1, 12)]
}
print(regression)
forecast_breakdown_df['regression'] = regression.get('regression')
forecast_breakdown_df.plot(x='t',
y=['one_step_forecast', 'demand', 'regression'])
plt.show()
def _generate_optimised_ses_forecast(self):
try:
demand = (list(self.__orders.get("demand")))
orders = [int(i) for i in demand]
# filter for lowest standard order and use for evo model
ses_evo_forecast = simple_exponential_smoothing_forecast(demand=orders, smoothing_level_constant=0.5,
optimise=True)
return ses_evo_forecast
except TypeError as e:
print('Exponential smoothing forecast (evolutionary model) failed. {}'.format(e))
def _generate_optimised_ses_forecast(self):
try:
demand = (list(self.__orders.get("demand")))
orders = [int(i) for i in demand]
ses_evo_forecast = simple_exponential_smoothing_forecast(
demand=orders, smoothing_level_constant=0.5)
return ses_evo_forecast
except TypeError as e:
print(
'Exponential smoothing forecast (evolutionary model) failed. {}'
.format(e))
except OSError as e:
print(e)
def test_simple_exponential_smoothing_key(self):
ses = simple_exponential_smoothing_forecast(demand=self._orders, alpha=0.5, forecast_length=6, initial_period=18)
for k in ses:
self.assertIn(k, self.ses_components)
