Your time series is worth a binary image: machine vision assisted deep framework for time series forecasting

TL;DR

This paper introduces a novel deep learning framework that transforms time series data into a binary image space for improved forecasting, leveraging machine vision techniques to outperform existing models.

Contribution

The paper proposes a unique binary machine vision space for time series analysis, enabling more effective deep learning-based forecasting without complex data preprocessing.

Findings

Outperforms state-of-the-art deep TSF models

Eliminates need for data decomposition or model customization

Demonstrates effectiveness through comprehensive analysis

Abstract

Time series forecasting (TSF) has been a challenging research area, and various models have been developed to address this task. However, almost all these models are trained with numerical time series data, which is not as effectively processed by the neural system as visual information. To address this challenge, this paper proposes a novel machine vision assisted deep time series analysis (MV-DTSA) framework. The MV-DTSA framework operates by analyzing time series data in a novel binary machine vision time series metric space, which includes a mapping and an inverse mapping function from the numerical time series space to the binary machine vision space, and a deep machine vision model designed to address the TSF task in the binary space. A comprehensive computational analysis demonstrates that the proposed MV-DTSA framework outperforms state-of-the-art deep TSF models, without requiring sophisticated data decomposition or model customization. The code for our framework is accessible at https://github.com/IkeYang/ machine-vision-assisted-deep-time-series-analysis-MV-DTSA-.