AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
This addresses the problem of automating time series regression for academia and industry, but it is incremental as it builds on existing AutoML and time series techniques.
The authors organized the first Automated Time Series Regression challenge (AutoSeries) to analyze time series with limited human effort, resulting in participants achieving significant performance improvements over a sample submission and competitive comparisons with AutoGluon using methods like feature engineering and LightGBM.
Analyzing better time series with limited human effort is of interest to academia and industry. Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present its design, analysis, and post-hoc experiments. The code submission requirement precluded participants from any manual intervention, testing automated machine learning capabilities of solutions, across many datasets, under hardware and time limitations. We prepared 10 datasets from diverse application domains (sales, power consumption, air quality, traffic, and parking), featuring missing data, mixed continuous and categorical variables, and various sampling rates. Each dataset was split into a training and a test sequence (which was streamed, allowing models to continuously adapt). The setting of time series regression, differs from classical forecasting in that covariates at the present time are known. Great strides were made by participants to tackle this AutoSeries problem, as demonstrated by the jump in performance from the sample submission, and post-hoc comparisons with AutoGluon. Simple yet effective methods were used, based on feature engineering, LightGBM, and random search hyper-parameter tuning, addressing all aspects of the challenge. Our post-hoc analyses revealed that providing additional time did not yield significant improvements. The winners' code was open-sourced https://github.com/NehzUx/AutoSeries.