Multiple seasonalities

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In this example we will show how to forecast data with multiple seasonalities using an MSTL.

Tip For this task, StatsForecast’s MSTL is 68% more accurate and 600% faster than Prophet and NeuralProphet. (Reproduce experiments here)

1. Install libraries
2. Load and explore the data
3. Fit a multiple-seasonality model
4. Decompose the series in trend and seasonality
5. Predict the next 24 hours
6. Optional: Forecast in production

Tip You can use Colab to run this Notebook interactively

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Install libraries

!pip install statsforecast

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Load Data

• The unique_id (string, int or category) represents an identifier for the series.
• The ds (datestamp or int) column should be either an integer indexing time or a datestamp ideally like YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp.
• The y (numeric) represents the measurement we wish to forecast. We will rename the

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/panambY/Hourly_Energy_Consumption/master/data/PJM_Load_hourly.csv')
df.columns = ['ds', 'y']
df.insert(0, 'unique_id', 'PJM_Load_hourly')
df = df.sort_values(['unique_id', 'ds']).reset_index(drop=True)
df.tail()

Note The StatsForecast.plot method uses matplotlib as a default engine. You can change to plotly by setting engine="plotly".

from statsforecast import StatsForecast

StatsForecast.plot(df)

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Fit an MSTL model

from statsforecast.models import MSTL, AutoARIMA

# Create a list of models and instantiation parameters

models = [MSTL(
    season_length=[24, 24 * 7], # seasonalities of the time series
    trend_forecaster=AutoARIMA() # model used to forecast trend
)]

• models: a list of models. Select the models you want from models and import them.
• freq: a string indicating the frequency of the data. (See panda’s available frequencies.)

sf = StatsForecast(
    models=models, # model used to fit each time series
    freq='h', # frequency of the data
)

Tip StatsForecast also supports this optional parameter.

• n_jobs: n_jobs: int, number of jobs used in the parallel processing, use -1 for all cores. (Default: 1)
• fallback_model: a model to be used if a model fails. (Default: none)

sf = sf.fit(df=df)

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Decompose the series

sf.fitted_[0, 0].model_

import matplotlib.pyplot as plt

sf.fitted_[0, 0].model_.tail(24 * 28).plot(subplots=True, grid=True)
plt.tight_layout()
plt.show()

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Predict the next 24 hours

Probabilistic forecasting with levels

• h (int): represents the forecast h steps into the future. In this case, 12 months ahead.
• level (list of floats): this optional parameter is used for probabilistic forecasting. Set the level (or confidence percentile) of your prediction interval. For example, level=[90] means that the model expects the real value to be inside that interval 90% of the times.

forecasts = sf.predict(h=24, level=[90])
forecasts.head()

sf.plot(df, forecasts, max_insample_length=24 * 7)

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Forecast in production

• h (int): represents the forecast h steps into the future. In this case, 12 months ahead.
• level (list of floats): this optional parameter is used for probabilistic forecasting. Set the level (or confidence percentile) of your prediction interval. For example, level=[90] means that the model expects the real value to be inside that interval 90% of the times.

forecasts_df = sf.forecast(df=df, h=24, level=[90])
forecasts_df.head()

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References

• Bandara, Kasun & Hyndman, Rob & Bergmeir, Christoph. (2021). “MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns”.

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Next Steps

• Learn how to use cross-validation to assess the robustness of your model