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Exploring Generative Data Augmentation in Multivariate Time Series Forecasting : Opportunities and Challenges - 2021

Exploring Generative Data Augmentation In Multivariate Time Series Forecasting : Opportunities And Challenges

Research Area:  Machine Learning

Abstract:

In multivariate time series (MTS), each time point constitutes multiple time-dependent variables. Short-term and long-term correlation of these variables is a significant characteristic of MTS, and is a key challenge while modelling the same. While classical auto-regressive models are heavily used to model MTS, neural models are more flexible and efficient. However, neural models rely on a large amount of labelled data for training. Availability of labelled time series data could be a bottleneck in real-world scenarios. This scarcity of labelled data can be mitigated by data augmentation. In MTS, augmentation techniques need to realize short-term correlations and long-term temporal dynamics. In this work, we introduce a novel meta-algorithm for time-series data augmentation to address the data scarcity problem. Due to the intrinsic ordering of samples in time series, we argue that one cannot simply add synthetic samples to the real samples for augmentation. To this end, we generate synthetic MTS data preserving temporal dynamics using an offthe-shelf generative algorithm and frame augmentation in MTS as a transfer learning problem. In addition, we point out the drawbacks of generative model in MTS augmentation. We show the effectiveness of our method on publicly available MTS datasets in forecasting. We also perform qualitative and quantitative analysis of synthetic MTS data and its applicability in long-term forecasting. To the best of our knowledge, this is the first study on generative data augmentation for MTS forecasting.

Keywords:  

Author(s) Name:  Ankur Debnath, Govind Waghmare, Hardik Wadhwa, Siddhartha Asthana, Ankur Arora

Journal name:  Workshop on Mining and Learning from Time Series

Conferrence name:  

Publisher name:  ACM

DOI:  https://doi.org/10. 1145/1122445.1122456

Volume Information: