MARINA: An MLP-Attention Model for Multivariate Time-Series Analysis

Abstract

The proliferation of real-time monitoring applications such as Artificial Intelligence for IT Operations (AIOps) and the Internet of Things (IoT) has led to the generation of a vast amount of time-series data. To extract the underlying value of the data, both the industry and the academia are in dire need of efficient and effective methods for time-series analysis. To this end, in this paper, we propose a Multi-layer perceptron ( M LP)- a ttention based multivariate time-se ri es a na lysis model MARINA. MARINA is designed to simultaneously learn the temporal and spatial correlations among multivariate time-series. Also, the model is versatile in that it is suitable for major time-series analysis tasks such as forecasting and anomaly detection. Through extensive comparisons with the representative multivariate time-series forecasting and anomaly detection algorithms, MARINA is shown to achieve state-of-the-art (SOTA) performance in both forecasting and anomaly detection tasks.