Building a Multivariate Time Series Benchmarking Datasets Inspired by Natural Language Processing (NLP)

TL;DR

This paper introduces a new benchmark dataset for multivariate time series analysis inspired by NLP datasets, aiming to improve model development and evaluation in various domains.

Contribution

It adapts NLP benchmark creation methodologies to time series data, emphasizing dataset diversity, relevance, and complexity, and explores multi-task learning strategies for better model performance.

Findings

Proposed a comprehensive, domain-relevant time series benchmark dataset.

Demonstrated the effectiveness of multi-task learning on the new dataset.

Highlighted the importance of data diversity and complexity in benchmarking.

Abstract

Time series analysis has become increasingly important in various domains, and developing effective models relies heavily on high-quality benchmark datasets. Inspired by the success of Natural Language Processing (NLP) benchmark datasets in advancing pre-trained models, we propose a new approach to create a comprehensive benchmark dataset for time series analysis. This paper explores the methodologies used in NLP benchmark dataset creation and adapts them to the unique challenges of time series data. We discuss the process of curating diverse, representative, and challenging time series datasets, highlighting the importance of domain relevance and data complexity. Additionally, we investigate multi-task learning strategies that leverage the benchmark dataset to enhance the performance of time series models. This research contributes to the broader goal of advancing the state-of-the-art in time series modeling by adopting successful strategies from the NLP domain.