A novel associative model for time series data mining

López-Yáñez, Itzamá; Sheremetov, Leonid; Yáñez-Márquez, Cornelio

doi:10.1016/j.patrec.2013.11.008

Cited by 34 publications

(22 citation statements)

References 23 publications

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“…In this paper, we have used the widely used software called Rapidminer to preprocess and analyze the stock prices as it supports all steps of data mining process [22]. Effort to predict the trend and pattern of stock market has been a very challenging one [23][24][25][26][27]. In Rapidminer software, data analysis is usually performed using graphs, plots, charts and tables in which one can easily visualize the output and also compare between one or more attributes and models.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

Chowdhury

Mahdy

Alam

et al. 2020

Physica A: Statistical Mechanics and its Applications

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Section: Machine Learning Approachmentioning

confidence: 99%

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

Chowdhury

Mahdy

Alam

et al. 2020

Physica A: Statistical Mechanics and its Applications

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“…Preliminaries Table II shows the Alpha and Beta operators, which were introduced in [23,24]. They are the basis for the development of Alpha-Beta memories, the Gamma classifier, passing through models such as the Alpha-Beta-BAM [24], the Alpha-Beta support vector machines [25], and many other models [26][27][28][29][30][31][32][33][34][35].…”

Section: Table-ii: Alpha and Beta Operatorsmentioning

confidence: 99%

Sensing and Forecasting of Pollution Data in Mexico City

Molina,

Cruz,

Vega

2019

IJEAT

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In this paper we present the characteristics of sensors used to monitor the pollution levels in Mexico City, namely sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3), , and carbon monoxide (CO). A novel algorithm to predict contamination levels is presented: the Gamma classifier. Also, a new coding technique is introduced, allowing the conversion from a series of values taken from SIMAT databases into a set of patterns, which in turn are useful for the task of pollutant forecasting. Experimental results show a competitive performance by the Gamma classifier as a predictor, when compared to other methods.

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“…As we know, at the minimum * of loss function ( ), the gradient of ( ) with respect to will be zero. Substituting (11) into (10), we can obtain…”

Section: The Levenberg-marquardt Algorithmmentioning

confidence: 99%

“…, ( − ) ( Figure 2). Until now, there are many literatures about the Mackey-Glass chaotic time series prediction [9][10][11][12][13][14]. However, as far as the prediction accuracy is concerned, most of the results in the literature are not ideal.…”

Section: Introductionmentioning

confidence: 99%

Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction

Zhao

et al. 2014

Discrete Dynamics in Nature and Society

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For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. When the multilayer perceptron is used to predict the Mackey-Glass chaotic time series, what we should do is to minimize the loss function. As is well known, the convergence speed of the loss function is rapid in the beginning of the learning process, while the convergence speed is very slow when the parameter is near to the minimum point. In order to overcome these problems, we introduce the Levenberg-Marquardt algorithm (LMA). Firstly, a rough introduction is given to the multilayer perceptron, including the structure and the model approximation method. Secondly, we introduce the LMA and discuss how to implement the LMA. Lastly, an illustrative example is carried out to show the prediction efficiency of the LMA. Simulations show that the LMA can give more accurate prediction than the gradient descent method.

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A novel associative model for time series data mining

Cited by 34 publications

References 23 publications

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model

Sensing and Forecasting of Pollution Data in Mexico City

Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction

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