def test_issue_286():
mod = ARIMA(order=(1, 1, 2))
mod.fit(wineind)
with pytest.raises(ValueError) as ve:
mod.predict_in_sample(start=0)
assert "In-sample predictions undefined for" in pytest_error_str(ve)
def test_with_seasonality1():
fit = ARIMA(order=(1, 1, 1),
seasonal_order=(0, 1, 1, 12),
suppress_warnings=True).fit(y=wineind)
_try_get_attrs(fit)
# R code AIC result is ~3004
assert abs(fit.aic() - 3004) < 100 # show equal within 100 or so
# R code AICc result is ~3005
assert abs(fit.aicc() - 3005) < 100 # show equal within 100 or so
# R code BIC result is ~3017
assert abs(fit.bic() - 3017) < 100 # show equal within 100 or so
# show we can predict in-sample
fit.predict_in_sample()
# test with SARIMAX confidence intervals
fit.predict(n_periods=10, return_conf_int=True, alpha=0.05)
# plt.figure()
# fitted_values = arima.predict_in_sample()
# plt.plot(df.index[:train_len - 1], fitted_values,
# color='C0', label="Fitted values")
# plt.plot(pd.to_datetime(df.index), data, color='C1', label="Data")
# plt.plot(df.index[:train_len - 1], arima.resid(),
# color='C2', label="Residuals")
# plt.gca().grid(which='both', axis='x', linestyle='--')
# plt.title("Residuals and fitted values")
# plt.legend()
print("SSE: {}".format((arima.resid()**2).sum()))
# Plot fitted values and forecasts
predictions = arima.predict(n_periods=test.shape[0])
fitted_values = arima.predict_in_sample()
plt.figure()
plt.plot(df.index[train_len:], test, '--', color='C0', label="test set")
plt.plot(df.index[train_len:],
predictions,
'--',
color='C1',
label="forecasted values")
plt.plot(df.index[:train_len], train, color='C0', label="train set")
plt.plot(df.index[:train_len - 1],
fitted_values,
color='C1',
label="fitted values")
plt.legend()
plt.title("Fitted values and forecasts")
def model_plot(days):
days = int(days)
pd.plotting.register_matplotlib_converters()
df = pd.read_csv('data/new_york.csv')
df['Date'] = pd.to_datetime(df['Date'])
#converting data to daily usage.
df.index = df.Date
df = df.drop('Date', axis=1)
# resample the dataframe every 1 day (D) and sum ovr each day
df = df.resample('D').sum()
df = df.tz_localize(None)
nyc_weather = pd.read_csv('data/weather/weatherNY.csv')
nyc_weather['DATE'] = pd.to_datetime(nyc_weather['DATE'])
nyc_weather = nyc_weather.set_index('DATE')
nyc_weather.drop(['NAME','STATION'],axis=1,inplace=True)
nyc_weather = nyc_weather['2015-07-01':'2020-08-10']
df = df[:'2020-08-10']
#trying 1 day increments with EXOG. MAYBE BEST CANDIDATE? with fourier terms june to june as 638 and august to august 516
day = days
real_values = []
predictions = []
df1 = df["2016":"2019"]
nyc_weather = nyc_weather["2016":"2019"]
y = df1.Consumption
exog = pd.DataFrame({'date': y.index})
exog = exog.set_index(pd.PeriodIndex(exog['date'], freq='D'))
exog['is_weekend'] = np.where(exog.index.dayofweek < 5,0,1)
#add weather data
exog['TMIN'] = nyc_weather['TMIN'].values
exog['sin1'] = np.sin(2 * np.pi * exog.index.dayofyear / 638)
exog['cos1'] = np.cos(2 * np.pi * exog.index.dayofyear / 638)
exog['sin2'] = np.sin(4 * np.pi * exog.index.dayofyear /638)
exog['cos2'] = np.cos(4 * np.pi * exog.index.dayofyear /638)
exog['sin3'] = np.sin(2 * np.pi * exog.index.dayofyear / 516)
exog['cos3'] = np.cos(2 * np.pi * exog.index.dayofyear / 516)
exog['sin4'] = np.sin(4 * np.pi * exog.index.dayofyear /516)
exog['cos4'] = np.cos(4 * np.pi * exog.index.dayofyear /516)
exog = exog.drop(columns=['date'])
num_to_update = 0
y_to_train = y.iloc[:(len(y)-100)]
exog_to_train = exog.iloc[:(len(y)-100)]
dates = []
steps = []
for i in range(5):
#first iteration train the model
if i == 0:
arima_exog_model = ARIMA(order=(3, 0, 1), seasonal_order=(2, 0, 0, 7),exogenous=exog_to_train, error_action='ignore',
initialization='approximate_diffuse', suppress_warnings=True).fit(y=y_to_train)
preds = arima_exog_model.predict_in_sample(exog_to_train)
#first prediction
y_to_test = y.iloc[(len(y)-100):(len(y)-100+day)]
y_exog_to_test = exog.iloc[(len(y)-100):(len(y)-100+day)]
y_arima_exog_forecast = arima_exog_model.predict(n_periods=day, exogenous=y_exog_to_test)
real_values.append(y_to_test.values)
predictions.append(y_arima_exog_forecast.tolist())
dates.append(y_to_test.index)
steps.append(y_to_test.index[-1])
#y_arima_exog_forecast = arima_exog_model.predict(n_periods=2, exogenous=exog_to_test)
else:
y_to_update = y.iloc[(len(y)-100+num_to_update):(len(y)-100+num_to_update)+day]
exog_to_update = exog.iloc[(len(y)-100+num_to_update):(len(y)-100+num_to_update)+day]
#to test
to_test = y.iloc[(len(y)-100+num_to_update)+day:(len(y)-100+num_to_update)+(day*2)]
exog_to_test = exog.iloc[(len(y)-100+num_to_update)+day:(len(y)-100+num_to_update)+(day*2)]
#update the model
arima_exog_model.update(y_to_update,exogenous=exog_to_update)
y_arima_exog_forecast = arima_exog_model.predict(n_periods=day, exogenous=exog_to_test)
dates.append(to_test.index)
steps.append(to_test.index[-1])
predictions.append(y_arima_exog_forecast.tolist())
real_values.append(to_test.values)
num_to_update += day
predict = [item for sublist in predictions for item in sublist]
true = [item for sublist in real_values for item in sublist]
dates = [item for sublist in dates for item in sublist]
#for viz purposes
y_to_train2 = y_to_train[-200:]
preds = preds[-200:]
y_to_train2 = y_to_train2.to_frame()
fig = go.Figure()
# Create and style traces
fig.add_trace(go.Scatter(x=y_to_train2.index, y=y_to_train2.Consumption, name='True values',
line=dict(color='firebrick', width=4,dash='dot')))
fig.add_trace(go.Scatter(x=y_to_train2.index, y=preds[-200:], name='In-sample Prediction',
line=dict(color='royalblue', width=4)))
fig.add_trace(go.Scatter(x=dates, y=predict, name='Prediction',
line=dict(color='green', width=4)))
fig.add_trace(go.Scatter(x=dates, y=true, name='True',
line=dict(color='firebrick', width=4,dash='dot')))
fig.update_layout(title='Electricity Consumption in New York',
xaxis_title='Date',
yaxis_title='Consumption',
xaxis_showgrid=True,
yaxis_showgrid=True,
#autosize=False,
#width=500,
#height=500,
paper_bgcolor=app_colors['background'],
plot_bgcolor=app_colors['background'])
return fig
