A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Abstract

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

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

“…In the field of time series analysis, time series prediction is an important research topic. Predicting the data trend can help users make reasonable decisions and plans, which is widely used in finance [1,2], energy [3], transportation [4], meteorology [5], and other fields. Time series prediction methods can be roughly classified into two categories: linear and nonlinear prediction.…”

“…, t n ) and a piecewise linear function function (), the time series T can be represented as shown in Eq. (1).…”

“…Time series data, a collection of data recorded at specific intervals over a given period, are prevalent in various domains, such as finance [1,2], energy [3], transportation [4], and meteorology [5]. Time series prediction is used to forecast future events by analyzing the trends of the past based on the assumption that future trends will be similar to historical trends [6].…”