Fast, effective, and coherent time series modeling using the sparsity-ranked lasso

TL;DR

This paper introduces a fast and effective time series modeling method using the sparsity-ranked lasso (SRL), which prioritizes simpler models and improves prediction accuracy, especially in complex seasonal data.

Contribution

The paper develops a novel SRL-based approach for time series modeling, incorporating ranked skepticism for complex terms and providing an efficient implementation via the fastTS R package.

Findings

SRL outperforms competitors in speed and accuracy

Method effectively handles uncertain and multiple seasonalities

Software implementation is scalable for large datasets

Abstract

The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more skeptical of higher-order polynomials and interactions *a priori* compared to main effects, and hence the inclusion of these more complex terms should require a higher level of evidence. In time series, the same idea of ranked prior skepticism can be applied to the possibly seasonal autoregressive (AR) structure of the series during the model fitting process, becoming especially useful in settings with uncertain or multiple modes of seasonality. The SRL can naturally incorporate exogenous variables, with streamlined options for inference and/or feature selection. The fitting process is quick even for large series with a high-dimensional feature set. In this work, we discuss both the formulation of this procedure and the software we have developed for its implementation via the **fastTS** R package. We explore the performance of our SRL-based approach in a novel application involving the autoregressive modeling of hourly emergency room arrivals at the University of Iowa Hospitals and Clinics. We find that the SRL is considerably faster than its competitors, while producing more accurate predictions.