scholarly journals Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

2018 ◽  
Author(s):  
Sang Hyuk Kim ◽  
Hee Soo Lee ◽  
Hanjun Ko ◽  
Seung Hwan Jeong ◽  
Hyun Woo Byun ◽  
...  
Keyword(s):  
Financial Markets ◽  
Real Time ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Series Data ◽  
Time Warping ◽  
Trading System ◽  
Index Futures ◽  
Data Movement ◽  
Dynamic Time

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.

scholarly journals Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 4641 ◽  
Author(s):  
Sang Kim ◽  
Hee Lee ◽  
Han Ko ◽  
Seung Jeong ◽  
Hyun Byun ◽  
...  
Keyword(s):  
Real Time ◽  
Pattern Matching ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Trading System ◽  
Index Futures ◽  
Data Movement ◽  
Dynamic Time

The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon’s clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Financial market investors are able to make more efficient investment strategies by using the PMTS. In this sense, the system developed in this paper contributes the efficiency of the financial markets and helps to achieve sustained economic growth.

Real-Time Fire Detection Algorithm Based on Support Vector Machine with Dynamic Time Warping Kernel Function

2021 ◽  
Author(s):  
Jaeseung Baek ◽  
Taha J. Alhindi ◽  
Young-Seon Jeong ◽  
Myong K. Jeong ◽  
Seongho Seo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Kernel Function ◽  
Dynamic Time Warping ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Time Warping ◽  
Dynamic Time

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Warping ◽  
Trend Prediction ◽  
Residual Signal ◽  
Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

scholarly journals Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0197499 ◽  
Author(s):  
Yongli Liu ◽  
Jingli Chen ◽  
Shuai Wu ◽  
Zhizhong Liu ◽  
Hao Chao
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Free congruence: an exploration of expanded similarity measures for time series data

2021 ◽  
Author(s):  
Lucas Cassiel Jacaruso
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Similarity Measures ◽  
Distance Measures ◽  
Series Data ◽  
Time Warping ◽  
Point To Point ◽  
Dynamic Time ◽  
Point Distance

Abstract Time series similarity measures are highly relevant in a wide range of emerging applications including training machine learning models, classification, and predictive modeling. Standard similarity measures for time series most often involve point-to-point distance measures including Euclidean distance and Dynamic Time Warping. Such similarity measures fundamentally require the fluctuation of values in the time series being compared to follow a corresponding order or cadence for similarity to be established. Other existing approaches use local statistical tests to detect structural changes in time series. This paper is spurred by the exploration of a broader definition of similarity, namely one that takes into account the sheer numerical resemblance between sets of statistical properties for time series segments irrespectively of value labeling. Further, the presence of common pattern components between time series segments was examined even if they occur in a permuted order, which would not necessarily satisfy the criteria of more conventional point-to-point distance measures. The newly defined similarity measures were tested on time series data representing over 20 years of cooperation intent expressed in global media sentiment. Tests determined whether the newly defined similarity measures would accurately identify stronger resemblance, on average, for pairings of similar time series segments (exhibiting overall decline) than pairings of differing segments (exhibiting overall decline and overall rise). The ability to identify patterns other than the obvious overall rise or decline that can accurately relate samples is regarded as a first step towards assessing the value of the newly explored similarity measures for classification or prediction. Results were compared with those of Dynamic Time Warping on the same data for context. Surprisingly, the test for numerical resemblance between sets of statistical properties established stronger resemblance for pairings of decline years with greater statistical significance than Dynamic Time Warping on the particular data and sample size used.

scholarly journals DESIGNING TRANSLATION TOOL: BETWEEN SIGN LANGUAGE TO SPOKEN TEXT ON KINECT TIME SERIES DATA USING DYNAMIC TIME WARPING

SINERGI ◽  
2018 ◽  
Vol 22 (2) ◽  
pp. 91
Author(s):  
Zico Pratama Putera ◽  
Mila Desi Anasanti ◽  
Bagus Priambodo
Keyword(s):  
User Interface ◽  
Sign Language ◽  
Dynamic Time Warping ◽  
User Study ◽  
Hearing Impaired ◽  
Microsoft Kinect ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Gestures ◽  
Dynamic Time

The gesture is one of the most natural and expressive methods for the hearing impaired. Most researchers, however, focus on either static gestures, postures or a small group of dynamic gestures due to the complexity of dynamic gestures. We propose the Kinect Translation Tool to recognize the user's gesture. As a result, the Kinect Translation Tool can be used for bilateral communication with the deaf community. Since real-time detection of a large number of dynamic gestures is taken into account, some efficient algorithms and models are required. The dynamic time warping algorithm is used here to detect and translate the gesture. Kinect Sign Language should translate sign language into written and spoken words. Conversely, people can reply directly with their spoken word, which is converted into literal text together with the animated 3D sign language gestures. The user study, which included several prototypes of the user interface, was carried out with the observation of ten participants who had to gesture and spell the phrases in American Sign Language (ASL). The speech recognition tests for simple phrases have therefore shown good results. The system also recognized the participant's gesture very well during the test. The study suggested that a natural user interface with Microsoft Kinect could be interpreted as a sign language translator for the hearing impaired.

scholarly journals A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability

2016 ◽  
Vol 261 ◽  
pp. 97-109 ◽  
Author(s):  
Yingqiu Cao ◽  
Nikolai Rakhilin ◽  
Philip H. Gordon ◽  
Xiling Shen ◽  
Edwin C. Kan
Keyword(s):  
Real Time ◽  
Dynamic Time Warping ◽  
Neural Recording ◽  
Classification Method ◽  
Time Warping ◽  
Dynamic Time
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