Improving stock trend prediction through financial time series classification and temporal correlation analysis based on aligning change point
- 28 November 2022
- journal article
- research article
- Published by Springer Nature in Soft Computing
- Vol. 27 (7) , 3655-3672
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61573118)
- Science and Technology Planning Project of Shenzen Municipality (JCYJ20190806112210067)
This publication has 39 references indexed in Scilit:
- Comparison of transfer entropy methods for financial time seriesPhysica A: Statistical Mechanics and its Applications, 2017
- Short-term stock price forecasting based on similar historical patterns extraction2017
- Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returnsNeural Computing and Applications, 2017
- Deep learning for stock market prediction from financial news articles2017
- Stock market forecasting with financial micro-blog based on sentiment and time series analysisJournal of Shanghai Jiaotong University (Science), 2017
- Herd mentality in the stock market: On the role of idiosyncratic participants with heterogeneous informationInternational Review of Financial Analysis, 2016
- Off-line signature verification based on a new symbolic representation and dynamic time warping2016
- Quantifying StockTwits semantic terms’ trading behavior in financial markets: An effective application of decision tree algorithmsExpert Systems with Applications, 2015
- A prediction scheme using perceptually important points and dynamic time warpingExpert Systems with Applications, 2014
- Dynamic programming algorithm optimization for spoken word recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1978