Using Lagged Covariates

if 'google.colab' in str(get_ipython()):
    !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while
    #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes

import pandas as pd
from neuralprophet import NeuralProphet, set_log_level
set_log_level("ERROR")

data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
df_ercot = pd.read_csv(data_location + "energy/ERCOT_load.csv")
df_ercot.head()

	ds	COAST	EAST	FAR_WEST	NORTH	NORTH_C	SOUTHERN	SOUTH_C	WEST	y
0	2004-01-01 01:00:00	7225.09	877.79	1044.89	745.79	7124.21	1660.45	3639.12	654.61	22971.94
1	2004-01-01 02:00:00	6994.25	850.75	1032.04	721.34	6854.58	1603.52	3495.16	639.88	22191.52
2	2004-01-01 03:00:00	6717.42	831.63	1021.10	699.70	6639.48	1527.99	3322.70	623.42	21383.44
3	2004-01-01 04:00:00	6554.27	823.56	1015.41	691.84	6492.39	1473.89	3201.72	613.49	20866.56
4	2004-01-01 05:00:00	6511.19	823.38	1009.74	686.76	6452.26	1462.76	3163.74	613.32	20723.15

regions = list(df_ercot)[1:-1]

df_ercot['y'].isnull().sum()

1

Baseline Model

df = pd.DataFrame({"ds": df_ercot["ds"], "y": df_ercot["y"]})
m = NeuralProphet(
    learning_rate=0.1,
)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast)
param = m.plot_parameters()

3-steps ahead AR Model

df = pd.DataFrame({"ds": df_ercot["ds"], "y": df_ercot["y"]})
m = NeuralProphet(
    n_forecasts=3,
    n_lags=3,
    learning_rate=0.1,
)
m = m.highlight_nth_step_ahead_of_each_forecast(3)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast[-7*24:])
param = m.plot_parameters()

3-steps ahead AR and Lagged Regressors Model

df = df_ercot
m = NeuralProphet(
    n_forecasts=3,
    n_lags=3,
    learning_rate= 0.1,
)
m = m.add_lagged_regressor(names=regions) #, only_last_value=True)
m.highlight_nth_step_ahead_of_each_forecast(3)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast[-7*24:])
param = m.plot_parameters()

24-steps ahead Long AR Model

df = pd.DataFrame({"ds": df_ercot["ds"], "y": df_ercot["y"]})
m = NeuralProphet(
    n_forecasts=24,
    n_lags=7*24,
    learning_rate=0.1,
)
m.highlight_nth_step_ahead_of_each_forecast(24)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast[-7*24:])
param = m.plot_parameters()

24-steps ahead Long AR Model with last observation of Lagged Regressors

df = df_ercot
m = NeuralProphet(
    n_forecasts=24,
    n_lags=7*24,
    learning_rate=0.1,
)
m = m.add_lagged_regressor(names=regions, only_last_value=True)
m.highlight_nth_step_ahead_of_each_forecast(24)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast[-7*24:])
param = m.plot_parameters()

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1243: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast["{}{}".format(comp, forecast_lag)] = yhat

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1251: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast[comp] = yhat

24-steps ahead AR Model with full Lagged Regressors

df = df_ercot
m = NeuralProphet(
    n_forecasts=24,
    n_lags=24,
    learning_rate=0.01,
)
m = m.add_lagged_regressor(names=regions)
m.highlight_nth_step_ahead_of_each_forecast(24)
metrics = m.fit(df, freq="H")

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
# comp = m.plot_components(forecast[-7*24:])
param = m.plot_parameters()

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1243: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast["{}{}".format(comp, forecast_lag)] = yhat

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1251: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast[comp] = yhat

24-steps ahead Neural Model with Long AR

df = pd.DataFrame({"ds": df_ercot["ds"], "y": df_ercot["y"]})
m = NeuralProphet(
    n_forecasts=24,
    n_lags=7*24,
    learning_rate=0.01,
    num_hidden_layers=1,
    d_hidden=16,
)
m = m.highlight_nth_step_ahead_of_each_forecast(24)
metrics = m.fit(df, freq="H")

# metrics

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
comp = m.plot_components(forecast[-7*24:])
# param = m.plot_parameters()

24-steps ahead Neural Model with Long AR and Lagged Regressors

df = df_ercot
m = NeuralProphet(
    n_forecasts=24,
    n_lags=7*24,
    learning_rate=0.01,
    num_hidden_layers=1,
    d_hidden=16,
)
m = m.add_lagged_regressor(names=regions)#, only_last_value=True)
m = m.highlight_nth_step_ahead_of_each_forecast(24)
metrics = m.fit(df, freq="H")

# metrics

forecast = m.predict(df)
# fig = m.plot(forecast)
fig1 = m.plot(forecast[-365*24:])
fig2 = m.plot(forecast[-7*24:])
comp = m.plot_components(forecast[-7*24:])
# param = m.plot_parameters()

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1243: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast["{}{}".format(comp, forecast_lag)] = yhat

WARNING - (py.warnings._showwarnmsg) - /home/tabletop/github/neural_prophet/neuralprophet/forecaster.py:1251: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`
  df_forecast[comp] = yhat