Tutorial 6: Future regressors

To model future regressors, both past and future values of these regressors have to be known. So in contrast to the lagged regressors in the previous tutorial, future regressors also have a forecasted value for the future in addition to the historic values.

import pandas as pd

# Load the dataset for tutorial 4 with the extra temperature column
df = pd.read_csv("https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial04.csv")
df.head()

	ds	y	temperature
0	2015-01-01	64.92	277.00
1	2015-01-02	58.46	277.95
2	2015-01-03	63.35	278.83
3	2015-01-04	50.54	279.64
4	2015-01-05	64.89	279.05

fig = df.plot(x="ds", y=["y", "temperature"], figsize=(10, 6))

from neuralprophet import NeuralProphet, set_log_level

# Disable logging messages unless there is an error
set_log_level("ERROR")

# Model and prediction
m = NeuralProphet(
    # Disable trend changepoints
    n_changepoints=10,
    # Disable seasonality components
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=True,
    # Add the autogression
    n_lags=10,
)
m.set_plotting_backend("plotly-static")

# Add the new future regressor
m.add_future_regressor("temperature")

# Continue training the model and making a prediction
metrics = m.fit(df)
forecast = m.predict(df)
m.plot(forecast)

m.plot_components(forecast, components=["future_regressors"])

m.plot_parameters(components=["future_regressors"])

metrics

	MAE	RMSE	Loss	RegLoss	epoch
0	36.102451	45.784710	0.222547	0.0	0
1	33.973568	43.057194	0.200457	0.0	1
2	32.275692	41.063568	0.183792	0.0	2
3	29.880583	38.180260	0.161229	0.0	3
4	27.537054	35.115955	0.138702	0.0	4
...	...	...	...	...	...
168	4.842709	6.438596	0.005013	0.0	168
169	4.883170	6.518759	0.005086	0.0	169
170	4.834648	6.401788	0.005007	0.0	170
171	4.839586	6.429725	0.005016	0.0	171
172	4.868096	6.472027	0.005059	0.0	172

173 rows × 5 columns

df_residuals = pd.DataFrame({"ds": df["ds"], "residuals": df["y"] - forecast["yhat1"]})
fig = df_residuals.plot(x="ds", y="residuals", figsize=(10, 6))