def test_future_reg(self):
log.info("testing: Future Regressors")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS + 50)
m = NeuralProphet(
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
df["A"] = df["y"].rolling(7, min_periods=1).mean()
df["B"] = df["y"].rolling(30, min_periods=1).mean()
regressors_df_future = pd.DataFrame(data={
"A": df["A"][-50:],
"B": df["B"][-50:]
})
df = df[:-50]
m = m.add_future_regressor(name="A")
m = m.add_future_regressor(name="B", mode="multiplicative")
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df=df,
regressors_df=regressors_df_future,
n_historic_predictions=10,
periods=50)
forecast = m.predict(df=future)
if self.plot:
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
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(self):
log.info("testing: Lagged Regressors")
df = pd.read_csv(PEYTON_FILE)
m = NeuralProphet(
n_forecasts=3,
n_lags=7,
ar_sparsity=0.1,
# num_hidden_layers=2,
# d_hidden=64,
# yearly_seasonality=False,
# weekly_seasonality=False,
# daily_seasonality=False,
epochs=EPOCHS,
)
if m.n_lags > 0:
df["A"] = df["y"].rolling(7, min_periods=1).mean()
df["B"] = df["y"].rolling(30, min_periods=1).mean()
m = m.add_lagged_regressor(name="A")
m = m.add_lagged_regressor(name="B", only_last_value=True)
# 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_lag_reg(self):
log.info("testing: Lagged Regressors")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
m = NeuralProphet(
n_forecasts=2,
n_lags=3,
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(30, min_periods=1).mean()
m = m.add_lagged_regressor(names="A")
m = m.add_lagged_regressor(names="B", only_last_value=True)
metrics_df = m.fit(df, freq="D", validate_each_epoch=True)
future = m.make_future_dataframe(df, n_historic_predictions=10)
forecast = m.predict(future)
if self.plot:
print(forecast.to_string())
m.plot_last_forecast(forecast, include_previous_forecasts=5)
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_future_reg(self):
log.info("testing: Future Regressors")
df = pd.read_csv(PEYTON_FILE)
m = NeuralProphet(
n_forecasts=1,
n_lags=0,
epochs=EPOCHS,
)
df["A"] = df["y"].rolling(7, min_periods=1).mean()
df["B"] = df["y"].rolling(30, min_periods=1).mean()
m = m.add_future_regressor(name="A", regularization=0.5)
m = m.add_future_regressor(name="B",
mode="multiplicative",
regularization=0.3)
metrics_df = m.fit(df, freq="D")
regressors_df = pd.DataFrame(data={
"A": df["A"][:50],
"B": df["B"][:50]
})
future = m.make_future_dataframe(df=df,
regressors_df=regressors_df,
n_historic_predictions=10,
periods=50)
forecast = m.predict(df=future)
if self.plot:
# print(forecast.to_string())
# m.plot_last_forecast(forecast, include_previous_forecasts=3)
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_events(self):
log.info("testing: Events")
df = pd.read_csv(PEYTON_FILE)[-NROWS:]
playoffs = pd.DataFrame(
{
"event": "playoff",
"ds": pd.to_datetime(
[
"2008-01-13",
"2009-01-03",
"2010-01-16",
"2010-01-24",
"2010-02-07",
"2011-01-08",
"2013-01-12",
"2014-01-12",
"2014-01-19",
"2014-02-02",
"2015-01-11",
"2016-01-17",
"2016-01-24",
"2016-02-07",
]
),
}
)
superbowls = pd.DataFrame(
{
"event": "superbowl",
"ds": pd.to_datetime(["2010-02-07", "2014-02-02", "2016-02-07"]),
}
)
events_df = pd.concat((playoffs, superbowls))
m = NeuralProphet(
n_lags=2,
n_forecasts=30,
daily_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
# set event windows
m = m.add_events(
["superbowl", "playoff"], lower_window=-1, upper_window=1, mode="multiplicative", regularization=0.5
)
# add the country specific holidays
m = m.add_country_holidays("US", mode="additive", regularization=0.5)
history_df = m.create_df_with_events(df, events_df)
metrics_df = m.fit(history_df, freq="D")
future = m.make_future_dataframe(df=history_df, events_df=events_df, periods=30, n_historic_predictions=90)
forecast = m.predict(df=future)
log.debug("Event Parameters:: {}".format(m.model.event_params))
if self.plot:
m.plot_components(forecast)
m.plot(forecast)
m.plot_parameters()
plt.show()
def test_predict(self):
log.info("testing: Predict")
df = pd.read_csv(PEYTON_FILE, nrows=512)
m = NeuralProphet(
n_forecasts=3,
n_lags=5,
epochs=1,
)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df, periods=None, n_historic_predictions=len(df) - m.n_lags)
forecast = m.predict(future)
if self.plot:
m.plot_last_forecast(forecast, include_previous_forecasts=10)
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_air_data(self):
log.info("TEST air_passengers.csv")
df = pd.read_csv(AIR_FILE)
m = NeuralProphet(
n_changepoints=0,
yearly_seasonality=2,
seasonality_mode="multiplicative",
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
metrics = m.fit(df, freq="MS")
future = m.make_future_dataframe(df, periods=48, n_historic_predictions=len(df) - m.n_lags)
forecast = m.predict(future)
if self.plot:
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()
def test_no_trend():
log.info("testing: No-Trend")
df = pd.read_csv(PEYTON_FILE, nrows=512)
m = NeuralProphet(
growth="off",
yearly_seasonality=False,
weekly_seasonality=False,
daily_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, periods=60, n_historic_predictions=60)
forecast = m.predict(df=future)
if PLOT:
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
