Open Access

ARTICLE

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Haibo Li^*, Yongbo Yu, Zhenbo Zhao, Xiaokang Tang

College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

* Corresponding Author: Haibo Li. Email:

(This article belongs to the Special Issue: Transfroming from Data to Knowledge and Applications in Intelligent Systems)

Computers, Materials & Continua 2024, 78(1), 653-676. https://doi.org/10.32604/cmc.2023.046424

Received 30 September 2023; Accepted 20 November 2023; Issue published 30 January 2024

Abstract

Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy. Therefore, this paper proposes a short-term forecasting method for time series based on a multi-granularity event, MGE-SP (multi-granularity event-based short-term prediction). First, a methodological framework for MGE-SP established guides the implementation steps. The framework consists of three key steps, including multi-granularity event matching based on the LTF (latest time first) strategy, multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio, and a short-term prediction model based on XGBoost. The data from a nationwide online car-hailing service in China ensures the method’s reliability. The average RMSE (root mean square error) and MAE (mean absolute error) of the proposed method are 3.204 and 2.360, lower than the respective values of 4.056 and 3.101 obtained using the ARIMA (autoregressive integrated moving average) method, as well as the values of 4.278 and 2.994 obtained using k-means-SVR (support vector regression) method. The other experiment is conducted on stock data from a public data set. The proposed method achieved an average RMSE and MAE of 0.836 and 0.696, lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method, as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

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Keywords

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