def forecast_plots(data):
fig = plt.figure(figsize=(12.8, 9.6), dpi=250)
ax = fig.add_subplot(1, 1, 1)
offset = 500
data.index = pd.to_datetime(data.index, utc=True)
train = data.iloc[100 + offset:200 + offset]
train.index = pd.to_datetime(train.index)
test = data.iloc[200 + offset:210 + offset]
test.index = pd.to_datetime(test.index)
model = SimpleExpSmoothing(np.asarray(train['total load actual']))
model._index = pd.to_datetime(train.index)
colours = ["orange", "green"]
# Fit and plot two SES models, with alpha = 0.2, 0.4
for i in range(1, 3):
fit = model.fit(smoothing_level=str(0.2 * i))
pred = fit.forecast(9)
ax.plot(train.index,
fit.fittedvalues,
label=u'\u03B1' + " = " + str(0.1 * i),
color=colours[i - 1])
ax.plot(test.index, pred, color=colours[i - 1])
# Fit and plot the optimal SES model - statsmodels finds the optimal alpha
optimised_fit = model.fit(optimized=True)
optimised_pred = optimised_fit.forecast(9)
ax.plot(train.index,
optimised_fit.fittedvalues,
label=u'\u03B1' + " = " +
str(optimised_fit.params['smoothing_level'])[:3],
color="darkkhaki")
ax.plot(test.index, optimised_pred, color="darkkhaki")
# Plot the training and test values
ax.plot(train.index,
train['total load actual'],
label="Training Data",
marker="o")
ax.plot(test.index,
test['total load actual'],
color="grey",
marker="o",
label="Test Data")
# Add legend and show plot
ax.legend(loc="best")
plt.show()
specProd.groupby(specProd['Date'].dt.strftime('%Y/%m/%d'))['Demand'].max())
noOrdersByDay['Day'] = noOrdersByDay.index
noOrdersByDay['Day'] = pd.to_datetime(noOrdersByDay['Day'], format='%Y/%m/%d')
byDay = pd.concat([noOrdersByDay, demandByDay], axis=1)
byDay = byDay.sort_values(by=['Day'])
plt.scatter(byDay['Day'], byDay['Orders'])
plt.show()
plt.scatter(byDay['Day'], byDay['Demand'])
train, test = train_test_split(byDay['Demand'], shuffle=False)
model = SimpleExpSmoothing(np.asarray(byDay['Demand']))
model._index = pd.to_datetime(train.index)
fit1 = model.fit()
pred1 = fit1.forecast(test)
fit2 = model.fit(smoothing_level=.2)
pred2 = fit2.forecast(test)
fit3 = model.fit(smoothing_level=.5)
pred3 = fit3.forecast(test)
fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(train.index[150:], train.values[150:])
ax.plot(test.index, test.values, color="gray")
for p, f, c in zip((pred1, pred2, pred3), (fit1, fit2, fit3),
('#ff7823', '#3c763d', 'c')):
ax.plot(train.index[150:], f.fittedvalues[150:], color=c)
ax.plot(test.index,