research-article

Visual time series forecasting: an image-driven approach

Authors:

Manuela VelosoAuthors Info & Claims

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

Article No.: 22, Pages 1 - 9

https://doi.org/10.1145/3490354.3494387

Published: 04 May 2022 Publication History

Abstract

Time series forecasting is essential for agents to make decisions. Traditional approaches rely on statistical methods to forecast given past numeric values. In practice, end-users often rely on visualizations such as charts and plots to reason about their forecasts. Inspired by practitioners, we re-imagine the topic by creating a novel framework to produce visual forecasts, similar to the way humans intuitively do. In this work, we leverage advances in deep learning to extend the field of time series forecasting to a visual setting. We capture input data as an image and train a model to produce the subsequent image. This approach results in predicting distributions as opposed to pointwise values. We examine various synthetic and real datasets with diverse degrees of complexity. Our experiments show that visual forecasting is effective for cyclic data but somewhat less for irregular data such as stock price. Importantly, when using image-based evaluation metrics, we find the proposed visual forecasting method to outperform various numerical baselines, including ARIMA and a numerical variation of our method. We demonstrate the benefits of incorporating vision-based approaches in forecasting tasks - both for the quality of the forecasts produced, as well as the metrics that can be used to evaluate them.

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Cited By

Shabani MMagris MTzagkarakis GKanniainen JIosifidis A(2023)Predicting the state of synchronization of financial time series using cross recurrence plotsNeural Computing and Applications10.1007/s00521-023-08674-y35:25(18519-18531)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1007/s00521-023-08674-y

Index Terms

Visual time series forecasting: an image-driven approach
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Published In

cover image ACM Conferences

ICAIF '21: Proceedings of the Second ACM International Conference on AI in Finance

November 2021

450 pages

ISBN:9781450391481

DOI:10.1145/3490354

General Chairs:
Anisoara Calinescu
University of Oxford, UK
,
Lukasz Szpruch
The Alan Turing Institute and University of Edinburgh, UK

Copyright © 2021 ACM.

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Published: 04 May 2022

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Cited By

Shabani MMagris MTzagkarakis GKanniainen JIosifidis A(2023)Predicting the state of synchronization of financial time series using cross recurrence plotsNeural Computing and Applications10.1007/s00521-023-08674-y35:25(18519-18531)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1007/s00521-023-08674-y

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