AutoFITS: Automatic Feature Engineering for Irregular Time Series

TL;DR

AutoFITS introduces an automatic feature engineering framework that leverages the timing of observations in irregular time series to enhance forecasting accuracy, offering a novel approach that complements existing methods.

Contribution

The paper presents a new framework for feature engineering in irregular time series, focusing on the timing of observations to improve forecasting performance.

Findings

Timing information significantly improves forecasting accuracy.

The framework outperforms traditional regularization methods.

Complementary to state-of-the-art forecasting techniques.

Abstract

A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are observed in uneven time intervals the time series is referred to as irregular or intermittent. In such scenarios, the most common solution is to reconstruct the time series to make it regular, thus removing its intermittency. We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance. We study this idea by developing a novel automatic feature engineering framework, which focuses on extracting information from this point of view, i.e., when each instance is collected. We study how valuable this information is by integrating it in a time series forecasting workflow and investigate how it compares to or complements state-of-the-art methods for regular time series forecasting. In the end, we contribute by providing a novel framework that tackles feature engineering for time series from an angle previously vastly ignored. We show that our approach has the potential to further extract more information about time series that significantly improves forecasting performance.