Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

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

Entropy ◽

10.3390/e23060731 ◽

2021 ◽

Vol 23 (6) ◽

pp. 731

Author(s):

Mengxia Liang ◽

Xiaolong Wang ◽

Shaocong Wu

Keyword(s):

Time Series ◽

Correlation Analysis ◽

Stock Price ◽

Multivariate Time Series ◽

Temporal Features ◽

Proposed Model ◽

Time Series Segmentation ◽

Similarity Model ◽

Dynamic Time ◽

Investment Portfolios

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.

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Multivariate time series prediction of high dimensional data based on deep reinforcement learning

E3S Web of Conferences ◽

10.1051/e3sconf/202125602038 ◽

2021 ◽

Vol 256 ◽

pp. 02038

Author(s):

Xin Ji ◽

Haifeng Zhang ◽

Jianfang Li ◽

Xiaolong Zhao ◽

Shouchao Li ◽

...

Keyword(s):

Time Series ◽

Reinforcement Learning ◽

Phase Space ◽

Time Series Prediction ◽

Multivariate Time Series ◽

High Dimensional Data ◽

Prediction Method ◽

Conditional Entropy ◽

High Dimensional ◽

Low Dimensional

In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

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SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining

Expert Systems with Applications ◽

10.1016/j.eswa.2019.112950 ◽

2020 ◽

Vol 141 ◽

pp. 112950 ◽

Cited By ~ 1

Author(s):

Hoonseok Park ◽

Jae-Yoon Jung

Keyword(s):

Time Series ◽

Association Rule ◽

Association Rule Mining ◽

Multivariate Time Series ◽

Pattern Discovery ◽

Rule Mining ◽

Symbolic Aggregate Approximation

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Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

Discrete Dynamics in Nature and Society ◽

10.1155/2008/526734 ◽

2008 ◽

Vol 2008 ◽

pp. 1-8 ◽

Cited By ~ 10

Author(s):

Junhai Ma ◽

Lixia Liu

Keyword(s):

Time Series ◽

Prediction Model ◽

Stock Returns ◽

Stock Price ◽

Linear Prediction ◽

Stock Exchange ◽

Multivariate Time Series ◽

Prediction Method ◽

Nonlinear Prediction ◽

Stock Price Prediction

This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.

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