Abstract
Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of data makes points in phase space clearly for researchers. Specially, a 2D structure of timeseries could combine the trend and correlation characters of different periods in timeseries together, which provides more clear information for top tasks. In this work, the effectiveness of 2D structure of timeseries is investigated in clustering tasks. There are 4 kinds of methods that the Recurrent Plot (RP), the Gramian Angular Summation Field (GASF), the Gramian Angular Differential Field (GADF) and the Markov Transition Field (MTF) have been adopted in the analysis. By classifying the CSI300 and S&P500 indexes, we found that the RP imaging series are valid in recognizing abnormal fluctuations of financial timeseries, as the silhouette values of clusters are over 0.6 to 1. Compared with segment methods, the 2D models have the lowest instability value of 0. It verifies that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks.
Funder
the Natural Science Foundation of Hubei Province
the Educational Commission of Hubei Province of China
the Hubei Key Laboratory of Applied Mathematics
the Doctoral Fund of Hubei University of Automotive Technology
Publisher
Public Library of Science (PLoS)
Reference47 articles.
1. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.;H. Wu;arXiv preprint arXiv:2210.02186,2022
2. Forecasting Model of Global Stock Index by Stochastic Time Effective Neural Network;Z. Liao;Expert Systems with Applications,2010
3. A Deep Increasing–Decreasing-Linear Neural Network for Financial Time Series Prediction.;A. de;Neurocomputing,2019
4. Behaviour of Stock Market Prices: Information Assimilation and Market Efficiency;J. Ramachandran;phdthesis,1985
5. Use and Misuse of Some Hurst Parameter Estimators Applied to Stationary and Non-Stationary Financial Time Series;F. Serinaldi;Physica A: Statistical Mechanics and its Applications,2010