Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

Kim, Sang Hyuk; Lee, Hee Soo; Ko, Hanjun; Jeong, Sangman; Byun, Hyun Woo; Oh, Kyong Joo

doi:10.20944/preprints201810.0660.v1

Cited by 10 publications

(3 citation statements)

References 20 publications

(25 reference statements)

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“…Speech recognition systems are based on the principles of recognition of recognition forms. The methods and algorithms that have been used so far can be divided into the following large classes [[6]- [7]]:…”

Section: Stage Of Classification Of Featuresmentioning

confidence: 99%

An Overview of the Recognition Algorithm of a Human Voice

Mamyrbayev¹,

Karelova²

2021

News of NAS RK. SPh-M

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Speech recognition has various applications, including human-machine interaction, sorting phone calls by gender classification, categorizing videos with tags, and so on. Currently, machine learning is a popular field that is widely used in various fields and applications, taking advantage of the latest developments in digital technologies and the advantages of data storage capabilities from electronic media. In this article, we will focus on voice gender recognition for a class of text-dependent systems using the Dynamic time distortion (DTW) algorithm and for a class of text-independent systems, the Gaussian mixture model. With this method, it is possible to distinguish a person's voice with the highest accuracy, since the components of Gaussian mixtures can simulate the personality of the voice. The article presents the results of testing the algorithm, and concludes that the Gaussian mixture model is applicable to solving the problem of identifying a person by voice.

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Section: Stage Of Classification Of Featuresmentioning

confidence: 99%

An Overview of the Recognition Algorithm of a Human Voice

Mamyrbayev¹,

Karelova²

2021

News of NAS RK. SPh-M

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“…У [14] Мейзі Лі, Янг Ксіан та ін. опублікували результати синтетичної оцінки різних алгоритмів машинного навчання, що ґрунтувалися на щоденних спостереженнях показників торгівлі акціями:…”

Section: рисунок 2 -фрагмент тренду з характерними поворотними точкамиunclassified

Models and Methods of Technical Analysis of Financial Markets

Ladyzhets,

Terenchuk

2021

MDCS

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This paper is devoted to the study of existing models and methods of analysis and prediction of financial markets. Basic information about fundamental and technical analysis of financial markets and their main assumptions is provided. The main tasks and problems that arise in the process of analysis and forecasting of financial markets are highlighted. The relevance of the topic is ensured by the fact of significant increase in the number of financial instruments in stock and other financial markets, as well as the rapid computerization of the trading process in these markets. Analysis of existing models and methods used to solve problems such as: analysis of the current market situation, search for patterns and anomalies in the financial time series and forecast the future price of the asset is provided. Authors mainly focus on statistical models and forecasting methods, pattern recognition methods, machine learning models and methods, sentimental analysis models and hybrid models. Study on the results of such models and methods as long short-term memory, gated recurrent units, support vector machine, perceptually important points is provided. In particular, given results of research of models that are used both independently and as components of a hybrid model for technical analysis of various financial markets. Namely, an overview of the achievements in the application of these models for short- and long-term forecasting in the United States stock market and Korean stock market. It has been found that hybrid artificial neural networks, which are able to take into account the public mood of market players, are the most promising for short-term forecasting of the company's stock price in the stock market. Based on the study, feasibility of using statistical models in combination with methods of pattern recognition or machine learning.

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“…This algorithm eliminates the mismatch between the x and y axes based on the transformation, this can be done when one point in a given time series depends on several points in another time series [10]. Another difficulty in implementing this algorithm is the difficulty of aligning these two lines when the corresponding points of the series are located above or below each other [11].…”

Section: Introductionmentioning

confidence: 99%

Algorithm For Identification Based On Voice

Narzillo*,

Nilufar,

Abdurashid

et al. 2019

IJITEE

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This article proposes an algorithm for automating the process of personality recognition based on voice, provides an analysis of existing methods used to solve the problem that needs to be solved. A method was implemented based on the Gaussian mixture model, which distinguishes a person’s voice with high accuracy. The components of this model allow you to simulate sound characteristics that are unique to each person. The results of the proposed algorithm and the use of voice recognition based on the results of the proposed algorithm are presented

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Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

Cited by 10 publications

References 20 publications

An Overview of the Recognition Algorithm of a Human Voice

An Overview of the Recognition Algorithm of a Human Voice

Models and Methods of Technical Analysis of Financial Markets

Algorithm For Identification Based On Voice

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