Fuzzy time-series prediction model based on text features and network features

• Published in: Neural computing & applications Vol. 35; no. 5; pp. 3639 - 3649
• Main Authors: Liu, Zeguang, Li, Yao, Liu, Huilin
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Forecasting; Fuzzy logic; Fuzzy sets; Image Processing and Computer Vision; Machine learning; Network topologies; Neural networks; Prediction models; Probability and Statistics in Computer Science; S.I. : Deep Geospatial Data Understanding; Set theory; Social networks; Special Issue on Deep Understanding of Big Geo-Social Data for Autonomous Vehicles; Statistical methods; Time series; Fuzzy model; Time-series predictions; Topological features extraction
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-05834-w

The prediction of time-series data is a challenging and complex issue. For many practical applications, network topology information and text information can play a perfect role in time-series prediction. This article takes stock data as an example by constructing a graph, connecting each stock’s upstream and downstream industries, and obtaining useful text features and topological features to predict the stock time-series. Based on the time-series data features, text features, and the topological features of the stock industry chain of machine learning, we compared our prediction model with other fuzzy time-series prediction methods, which are only based on historical features. The experiment shows that our method is better than the other methods in terms of the performance of multiple stocks in each stock’s time-series prediction. The experimental results show that the stock topology based on the industrial chain effectively improved time-series forecasting accuracy.