import numpy as np
import pandas as pd
from autogluon.timeseries.models.local.abstract_local_model import AbstractLocalModel, seasonal_naive_forecast
[docs]class NaiveModel(AbstractLocalModel):
"""Baseline model that sets the forecast equal to the last observed value.
Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is
estimated from the empirical distribution of the residuals.
As described in https://otexts.com/fpp3/prediction-intervals.html
"""
allowed_local_model_args = ["seasonal_period"]
def _predict_with_local_model(
self,
time_series: pd.Series,
local_model_args: dict,
) -> pd.DataFrame:
return seasonal_naive_forecast(
target=time_series.values.ravel(),
prediction_length=self.prediction_length,
quantile_levels=self.quantile_levels,
seasonal_period=1,
)
[docs]class SeasonalNaiveModel(AbstractLocalModel):
"""Baseline model that sets the forecast equal to the last observed value from the same season.
Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is
estimated from the empirical distribution of the residuals.
As described in https://otexts.com/fpp3/prediction-intervals.html
Other Parameters
----------------
seasonal_period : int or None, default = None
Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a
weekly cycle or 12 for monthly data with an annual cycle.
When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be
specified manually by providing an integer > 1.
If seasonal_period (inferred or provided) is equal to 1, will fall back to Naive forecast.
Seasonality will also be disabled, if the length of the time series is < seasonal_period.
"""
allowed_local_model_args = ["seasonal_period"]
def _predict_with_local_model(
self,
time_series: np.ndarray,
local_model_args: dict,
) -> pd.DataFrame:
return seasonal_naive_forecast(
target=time_series.values.ravel(),
prediction_length=self.prediction_length,
quantile_levels=self.quantile_levels,
seasonal_period=local_model_args["seasonal_period"],
)