Model selection confidence sets for time series models with applications to electricity load data

TL;DR

This paper introduces the Model Selection Confidence Set (MSCS) methodology for univariate time series, applied to Italian electricity load data, to quantify model uncertainty and identify key load drivers.

Contribution

The paper develops MSCS for time series, providing a new way to assess model uncertainty and interpret key factors influencing electricity load.

Findings

MSCS identifies a set of statistically indistinguishable models at a given confidence level.

Model uncertainty varies intraday, with larger sets during noisier periods.

Key load drivers include hourly lags, temperature, calendar effects, and solar energy.

Abstract

This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to study model selection uncertainty in the Italian electricity load data. Rather than relying on a single model selected by an arbitrary criterion, the MSCS identifies a set of models that are statistically indistinguishable from the true data-generating process at a given confidence level. The size and composition of this set reveal crucial information about model selection uncertainty: noisy data scenarios produce larger sets with many candidate models, while more informative cases narrow the set considerably. To study the importance of each model term, we consider numerical statistics measuring the frequency with which each term is included in both the entire MSCS and in Lower Boundary Models (LBM), its most parsimonious specifications. Applied to Italian hourly electricity load data, the MSCS methodology reveals marked intraday variation in model selection uncertainty and isolates a collection of model specifications that deliver competitive short-term forecasts while highlighting key drivers of electricity load like intraday hourly lags, temperature, calendar effects and solar energy generation.