A pattern representation of stock time series based on DTW

Han, Tian; Peng, Qinke; Zhu, Zhibo; Shen, Yiqing; Huang, Huijun; Abid, Nahiyoon Nabeel

doi:10.1016/j.physa.2020.124161

Cited by 21 publications

(11 citation statements)

References 21 publications

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“…Its algorithm compares two data series and performs calculations to find the optimum path between two data series through the data alignment process (Wang, Lyu, Shi, & Liang, 2018). In other words, the DTW distance is the minimum distance between two series by considering the possibility of a point shift (Han et al, 2020). As the advantages of DTW, this distance method is used because it can accommodate the condition of Covid-19 data in each sub-district that is identified for the first time at different times.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta

Yohansa¹,

Notodiputro²,

Erfiani³

2022

ComTech

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The number of positive cases of Covid-19 in DKI Jakarta has contributed to the national issues, reaching 25% of the total cases in Indonesia. The research examined and modeled the distribution pattern of Covid-19 positive cases in DKI Jakarta based on 44 districts spreading over six administrative areas. The data were regarding positive Covid-19 cases in DKI Jakarta for the past year, from April 2020 to April 2021. The research related to the pattern of positive Covid-19 distribution in 44 districts was carried out by time series clustering through Dynamic Time Warping (DTW) distances and agglomerative hierarchical methods. Then, the effectiveness of the clustering process is evaluated by comparing the predicted value of Covid-19 cases between clustering and non-clustering forecast results at the city level for the next 14 days through the Autoregressive Integrated Moving Average (ARIMA) model. The results group 44 districts into 6 optimal clusters based on the pattern of positive cases of Covid-19 in each district. The highest distribution rate is in cluster A, and the lowest is in cluster F. Geographical characteristics are also indicated by clusters A, B, E, and F. Then, the results show that the Mean Average Percentage Error (MAPE) value of the clustering model ranges from 16% to 20%. The difference between MAPE values to the non-clustering model implies that the forecasting accuracy is not far apart, which is in the round of 5%−6%.

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Section: Methodsmentioning

confidence: 99%

Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta

Yohansa¹,

Notodiputro²,

Erfiani³

2022

ComTech

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“…Compared with the Euclidean distance, dynamic time warping (DTW) [22][23][24][25][26] is a distance measurement method, in which it firstly calculates the distance matrix D between time series data and then uses the dynamic programming method to construct the cost matrix R. Finally, based on the matrix R, an optimal bending path W is found so that…”

Section: Preliminariesmentioning

confidence: 99%

“…However, most clustering algorithms [18][19][20][21] cannot solve nonspherical distribution of MTS data well. In the process of clustering, due to the lengths of most MTSs are different, it is necessary to use the dynamic time warping (DTW) [22][23][24][25][26] to measure the similarity between their corresponding component attributes. But the importance of component attributes has not yet received due attention.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

Wan

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.

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“…e experimental results also showed that the hybrid model incorporating the wavelet transform is better than the prediction using a single algorithm [29][30][31][32]. DTW is a kind of elastic measurement for calculating time series similarity [33][34][35]. As DTW can extract morphological features of time series and handle time transformations and distortions, it can be used for pattern representation of stock time series.…”

Section: Related Workmentioning

confidence: 99%

“…As DTW can extract morphological features of time series and handle time transformations and distortions, it can be used for pattern representation of stock time series. For example, Han developed a portfolio optimization model with strict constraints to obtain a pattern representation of stock time series based on DTW [35].…”

Section: Related Workmentioning

confidence: 99%

A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

Luo

2021

Mathematical Problems in Engineering

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The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.

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A pattern representation of stock time series based on DTW

Cited by 21 publications

References 21 publications

Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta

Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta

Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

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