def test_seasons(self):
log.info("testing: Seasonality")
df = pd.read_csv(PEYTON_FILE, nrows=512)
# m = NeuralProphet(n_lags=60, n_changepoints=10, n_forecasts=30, verbose=True)
m = NeuralProphet(
yearly_seasonality=8,
weekly_seasonality=4,
# daily_seasonality=False,
seasonality_mode="additive",
# seasonality_mode="multiplicative",
seasonality_reg=1,
epochs=EPOCHS,
)
metrics_df = m.fit(df, freq="D", validate_each_epoch=True)
future = m.make_future_dataframe(df,
n_historic_predictions=len(df),
periods=365)
forecast = m.predict(df=future)
log.debug("SUM of yearly season params: {}".format(
sum(abs(m.model.season_params["yearly"].data.numpy()))))
log.debug("SUM of weekly season params: {}".format(
sum(abs(m.model.season_params["weekly"].data.numpy()))))
log.debug("season params: {}".format(m.model.season_params.items()))
if self.plot:
m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_custom_changepoints(self):
log.info("testing: Custom Changepoints")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
dates = df["ds"][range(1, len(df) - 1, int(len(df) / 5.0))]
dates_list = [str(d) for d in dates]
dates_array = pd.to_datetime(dates_list).values
log.debug("dates: {}".format(dates))
log.debug("dates_list: {}".format(dates_list))
log.debug("dates_array: {} {}".format(dates_array.dtype, dates_array))
for cp in [dates_list, dates_array]:
m = NeuralProphet(
changepoints=cp,
yearly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
# print(m.config_trend)
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 self.plot:
# m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
plt.show()
def test_custom_seasons(self):
log.info("testing: Custom Seasonality")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
# m = NeuralProphet(n_lags=60, n_changepoints=10, n_forecasts=30, verbose=True)
other_seasons = False
m = NeuralProphet(
yearly_seasonality=other_seasons,
weekly_seasonality=other_seasons,
daily_seasonality=other_seasons,
seasonality_mode="additive",
# seasonality_mode="multiplicative",
seasonality_reg=1,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
m = m.add_seasonality(name="quarterly", period=90, fourier_order=5)
log.debug("seasonalities: {}".format(m.season_config.periods))
metrics_df = m.fit(df, freq="D", validate_each_epoch=True)
future = m.make_future_dataframe(df,
n_historic_predictions=365,
periods=365)
forecast = m.predict(df=future)
log.debug("season params: {}".format(m.model.season_params.items()))
if self.plot:
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_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_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, 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_trend(self):
log.info("testing: Trend")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
m = NeuralProphet(
growth="linear",
n_changepoints=10,
changepoints_range=0.9,
trend_reg=1,
trend_reg_threshold=False,
yearly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
# print(m.config_trend)
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 self.plot:
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)
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_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_yosemite(self):
log.info("TEST Yosemite Temps")
df = pd.read_csv(YOS_FILE, nrows=NROWS)
m = NeuralProphet(
changepoints_range=0.95,
n_changepoints=15,
weekly_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
metrics = m.fit(df, freq="5min")
future = m.make_future_dataframe(df, periods=12 * 24, n_historic_predictions=12 * 24)
forecast = m.predict(future)
if self.plot:
m.plot(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,
# trend_reg=1,
yearly_seasonality=2,
# seasonality_reg=1,
# seasonality_mode="additive",
seasonality_mode="multiplicative",
)
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)
m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
if self.plot:
plt.show()
def test_seasons():
log.info("testing: Seasonality: additive")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
# m = NeuralProphet(n_lags=60, n_changepoints=10, n_forecasts=30, verbose=True)
m = NeuralProphet(
yearly_seasonality=8,
weekly_seasonality=4,
seasonality_mode="additive",
seasonality_reg=1,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df,
n_historic_predictions=365,
periods=365)
forecast = m.predict(df=future)
log.debug("SUM of yearly season params: {}".format(
sum(abs(m.model.season_params["yearly"].data.numpy()))))
log.debug("SUM of weekly season params: {}".format(
sum(abs(m.model.season_params["weekly"].data.numpy()))))
log.debug("season params: {}".format(m.model.season_params.items()))
if PLOT:
m.plot(forecast)
# m.plot_components(forecast)
m.plot_parameters()
plt.show()
log.info("testing: Seasonality: multiplicative")
df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
# m = NeuralProphet(n_lags=60, n_changepoints=10, n_forecasts=30, verbose=True)
m = NeuralProphet(
yearly_seasonality=8,
weekly_seasonality=4,
seasonality_mode="multiplicative",
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
metrics_df = m.fit(df, freq="D")
future = m.make_future_dataframe(df,
n_historic_predictions=365,
periods=365)
forecast = m.predict(df=future)
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()
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_air_data():
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 PLOT:
m.plot(forecast)
m.plot_components(forecast)
m.plot_parameters()
plt.show()
def time_pattern():
global target, daypara, df, df2, df_4pycaret, df_temp
EPOCH = st.sidebar.slider("Epochs", 100, 1000)
model = NeuralProphet(
growth="linear",
changepoints=None,
n_changepoints=30,
changepoints_range=0.95,
trend_reg=0,
trend_reg_threshold=False,
yearly_seasonality="auto",
weekly_seasonality=True,
daily_seasonality="auto",
seasonality_mode="additive",
seasonality_reg=0,
n_forecasts=30,
n_lags=60, ##determines autoregression
num_hidden_layers=0,
d_hidden=None,
ar_sparsity=None,
learning_rate=None,
epochs=EPOCH,
loss_func="Huber",
normalize="auto",
impute_missing=True,
)
metrics = model.fit(df2, validate_each_epoch=True, freq="D")
future = model.make_future_dataframe(df2,
periods=252,
n_historic_predictions=len(df2))
with st.spinner("Training..."):
forecast = model.predict(future)
fig, ax = plt.subplots(1, 2, figsize=(17, 7))
ax[0].plot(metrics["MAE"], 'ob', linewidth=6, label="Training Loss")
ax[0].plot(metrics["MAE_val"],
'-r',
linewidth=2,
label="Validation Loss")
ax[0].legend(loc='center right')
ax[0].tick_params(axis='both', which='major')
ax[0].set_xlabel("Epoch")
ax[0].set_ylabel("Loss")
ax[0].set_title("Model Loss (MAE)")
ax[1].plot(metrics["SmoothL1Loss"],
'ob',
linewidth=6,
label="Training Loss")
ax[1].plot(metrics["SmoothL1Loss_val"],
'-r',
linewidth=2,
label="Validation Loss")
ax[1].legend(loc='center right')
ax[1].tick_params(axis='both', which='major')
ax[1].set_xlabel("Epoch")
ax[1].set_ylabel("Loss")
ax[1].set_title("Model Loss (SmoothL1Loss)")
st.subheader("Loss Check")
st.pyplot()
with st.spinner("Recognizing Time Pattern"):
st.subheader("Time Pattern")
model.plot_parameters()
st.set_option('deprecation.showPyplotGlobalUse', False)
st.pyplot()
weekly_seasonality=False,
batch_size=64,
epochs=10,
learning_rate=1.0,
)
metrics = m.fit(df, freq='5min')
future = m.make_future_dataframe(df, n_historic_predictions=True)
forecast = m.predict(future)
fig = m.plot(forecast)
plt.show()
fig = m.plot(forecast[144:6 * 288])
plt.show()
# fig_comp = m.plot_components(forecast)
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,
def test_global_modeling_no_exogenous_variable():
### GLOBAL MODELLING - NO EXOGENOUS VARIABLE
log.info("Global Modeling - No exogenous variables")
df = pd.read_csv(PEYTON_FILE, nrows=512)
df1_0 = df.iloc[:128, :].copy(deep=True)
df2_0 = df.iloc[128:256, :].copy(deep=True)
df3_0 = df.iloc[256:384, :].copy(deep=True)
df4_0 = df.iloc[384:, :].copy(deep=True)
train_input = {
0: df1_0,
1: {
"df1": df1_0,
"df2": df2_0
},
2: {
"df1": df1_0,
"df2": df2_0
}
}
test_input = {0: df3_0, 1: {"df1": df3_0}, 2: {"df1": df3_0, "df2": df4_0}}
info_input = {
0: "Testing df train / df test - no events, no regressors",
1: "Testing dict df train / df test - no events, no regressors",
2: "Testing dict df train / dict df test - no events, no regressors",
}
for i in range(0, 3):
log.info(info_input[i])
m = NeuralProphet(n_forecasts=2,
n_lags=10,
epochs=EPOCHS,
batch_size=BATCH_SIZE)
metrics = m.fit(train_input[i], freq="D")
forecast = m.predict(df=test_input[i])
forecast_trend = m.predict_trend(df=test_input[i])
forecast_seasonal_componets = m.predict_seasonal_components(
df=test_input[i])
if PLOT:
forecast = forecast if isinstance(forecast, list) else [forecast]
for key in forecast:
fig1 = m.plot(forecast[key])
fig2 = m.plot(forecast[key])
with pytest.raises(ValueError):
forecast = m.predict({"df4": df4_0})
log.info(
"Error - dict with names not provided in the train dict (not in the data params dict)"
)
with pytest.raises(ValueError):
metrics = m.test({"df4": df4_0})
log.info(
"Error - dict with names not provided in the train dict (not in the data params dict)"
)
m = NeuralProphet(
n_forecasts=2,
n_lags=10,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
m.fit({"df1": df1_0, "df2": df2_0}, freq="D")
with pytest.raises(ValueError):
forecast = m.predict({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
metrics = m.test({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
forecast_trend = m.predict_trend({"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
with pytest.raises(ValueError):
forecast_seasonal_componets = m.predict_seasonal_components(
{"df4": df4_0})
# log.info("unknown_data_normalization was not set to True")
# Set unknown_data_normalization to True - now there should be no errors
m.config_normalization.unknown_data_normalization = True
forecast = m.predict({"df4": df4_0})
metrics = m.test({"df4": df4_0})
forecast_trend = m.predict_trend({"df4": df4_0})
forecast_seasonal_componets = m.predict_seasonal_components({"df4": df4_0})
m.plot_parameters(df_name="df1")
m.plot_parameters()
# %%
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)
