def test_plot(self):
log.info("testing: Plotting")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
m = NeuralProphet(
n_forecasts=7,
n_lags=14,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
metrics_df = m.fit(df, freq="D")
m.highlight_nth_step_ahead_of_each_forecast(7)
future = m.make_future_dataframe(df, n_historic_predictions=10)
forecast = m.predict(future)
m.plot(forecast)
m.plot_last_forecast(forecast, include_previous_forecasts=10)
m.plot_components(forecast)
m.plot_parameters()
m.highlight_nth_step_ahead_of_each_forecast(None)
future = m.make_future_dataframe(df, n_historic_predictions=10)
forecast = m.predict(future)
m.plot(forecast)
m.plot_last_forecast(forecast)
m.plot_components(forecast)
m.plot_parameters()
if self.plot:
plt.show()
def test_lag_reg_deep(self):
log.info("testing: List of Lagged Regressors (deep)")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
m = NeuralProphet(
n_forecasts=1,
n_lags=14,
num_hidden_layers=2,
d_hidden=32,
weekly_seasonality=False,
daily_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
df["A"] = df["y"].rolling(7, min_periods=1).mean()
df["B"] = df["y"].rolling(15, min_periods=1).mean()
df["C"] = df["y"].rolling(30, min_periods=1).mean()
cols = [col for col in df.columns if col not in ["ds", "y"]]
m = m.add_lagged_regressor(names=cols)
m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
metrics_df = m.fit(df, freq="D", validate_each_epoch=True)
future = m.make_future_dataframe(df, n_historic_predictions=365)
forecast = m.predict(future)
if self.plot:
# print(forecast.to_string())
# m.plot_last_forecast(forecast, include_previous_forecasts=10)
# m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_ar_net(self):
log.info("testing: AR-Net")
df = pd.read_csv(PEYTON_FILE)
m = NeuralProphet(
n_forecasts=7,
n_lags=14,
# ar_sparsity=0.01,
# num_hidden_layers=0,
num_hidden_layers=2,
d_hidden=64,
# yearly_seasonality=False,
# weekly_seasonality=False,
# daily_seasonality=False,
epochs=EPOCHS,
)
m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
metrics_df = m.fit(df, freq="D", validate_each_epoch=True)
future = m.make_future_dataframe(df,
n_historic_predictions=len(df) -
m.n_lags)
forecast = m.predict(df=future)
if self.plot:
m.plot_last_forecast(forecast, include_previous_forecasts=3)
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_ar(self):
log.info("testing: AR")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
m = NeuralProphet(
n_forecasts=7,
n_lags=7,
yearly_seasonality=False,
epochs=EPOCHS,
# batch_size=BATCH_SIZE,
)
m.highlight_nth_step_ahead_of_each_forecast(m.n_forecasts)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df, n_historic_predictions=90)
forecast = m.predict(df=future)
if self.plot:
m.plot_last_forecast(forecast, include_previous_forecasts=3)
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
fig_param = m.plot_parameters()
plt.show()
'''
Larger forecast horizon¶
For predictions further into the future, you could reduce the resulution of the data. Using a 5-minute resolution may be useful for a high-resolution short-term forecast, but counter-productive for a long-term forecast. As we only have a limited amount of data (approx 2 months), we want to avoid over-specifying the model.
As an example: If we set the model to forecast 24 hours into the future (nforecasts=24*12) based on the last day's temperatures (n_lags=24*12), the number of parameters of our AR component grows to 24*12*24*12 = 82,944. However, we only have about 2*30*24*12 = 17,280 samples in our dataset. The model would be overspecified.
If we first downsample our data to hourly data, we reduce our dataset to 2*30*24=1440 and our model parameters to 24*24=576. Thus, we are able to fit the model. However, it would be better to collect more data.
'''
df.loc[:, "ds"] = pd.to_datetime(df.loc[:, "ds"])
df_hourly = df.set_index('ds', drop=False).resample('H').mean().reset_index()
m = NeuralProphet(
n_lags=24,
n_forecasts=24,
changepoints_range=0.95,
n_changepoints=30,
weekly_seasonality=False,
learning_rate=0.3,
)
metrics = m.fit(df_hourly, freq='H')
future = m.make_future_dataframe(df_hourly, n_historic_predictions=True)
forecast = m.predict(future)
fig = m.plot(forecast)
plt.show()
m = m.highlight_nth_step_ahead_of_each_forecast(24)
fig = m.plot_last_forecast(forecast, include_previous_forecasts=10)
plt.show()
# %%
model_arima.summary()
# %% Learning rates for ARNet
for loss in mae_np:
np.log(loss['SmoothL1Loss'][1:]).plot()
# %% Error against lag
final_loss = [loss['SmoothL1Loss'].iloc[-1] for loss in mae_np]
plt.plot(final_loss)
# %%
model_nprophet_ar.model.ar_weights.shape
mae_np[-1].plot()
model_nprophet_ar.model.bias
model_nprophet_ar = model_nprophet_ar.highlight_nth_step_ahead_of_each_forecast(1)
fig_param = model_nprophet_ar.plot_parameters()
model_nprophet_ar
# %%
fig = res.plot_diagnostics(fig=fig, lags=30)
fig = res.plot_predict(720, 840)
