A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction

Cui, Huimin; Feng, Jianxin; Guo, Jin; Wang, Tingfeng

doi:10.1016/j.knosys.2015.07.032

Cited by 31 publications

(17 citation statements)

References 47 publications

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“…In this paper, the application of the SCA-MNM on time prediction is illustrated using two mostly used datasets [7,14,16] Mackey-Glass (M-G) time series dataset, Box-Jenkins (B-J) gas furnace dataset. These data sets have been pre-processed by normalizing them between 0.1 and 0.9 like in 'SCA-MNM Algorithm'.…”

Section: Time Series Prediction Problemsmentioning

confidence: 99%

“…In the literature, some papers propose several NN with various structure of NN like [4][5][6][7][8][9], various training algorithms [10][11][12][13][14] or both [15,16]. MNM is comprised of only single neuron and demands less training time.…”

Section: Introductionmentioning

confidence: 99%

“…However, BPA can fall into the local optima easily. In the past few decades, a lot of optimization algorithms based on nature-inspired have been proposed in the literature, and to overcome above mentioned shortcomings of BPA, different evolutionary algorithms and its variants have been proposed in literature to train the MNM like PSO [14], glowworm swarm optimization [16], differential evolutionary algorithm [17] and harmony search algorithm [18]. Although a wide range of evolutionary algorithms are investigated in the literature for training NNs, the problem of local minima is still open.…”

Section: Introductionmentioning

confidence: 99%

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Multiplicative Neuron Model Based on Sine Cosine Algorithm for Time Series Prediction

Kolay¹

2019

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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Time series prediction is a method to predict the system behavior in the future based on current given data. Neural Networks (NNs) approach is a well-known technique that is useful for time series prediction. In the literature many NN models such as Multilayer Perceptron (MLP), Pi-Sigma NN (PSNN), Recurrent NN etc. are proposed for solving time series prediction. In this paper, we use Multiplicative Neuron Model (MNM) to predict time series. For training this model, we propose to use newly developed evolutionary optimization algorithm called Sine Cosine algorithm (SCA), and this algorithm has not been used as far as we know in training the MNM. The proposed SCA-MNM model is employed for the well-known time series problems. In this paper, the application of the SCA-MNM on time prediction is illustrated using two mostly used datasets Mackey-Glass time series dataset, Box-Jenkins gas furnace dataset. To investigate the effect of the proposed SCA-MNM model, comparisons are made with some of the results given in the literature.

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Section: Time Series Prediction Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiplicative Neuron Model Based on Sine Cosine Algorithm for Time Series Prediction

Kolay¹

2019

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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“…The tested functions are widely used by researchers [34][35][36][37]. These functions are: Tables 11, 12 and 13 demonstrate that Multi-pop-ABC performs better than the compared ABC, PS-ABC and PS-ABCII algorithms [34][35][36] in terms of both mean and standard deviation (SD). Note that the best results are highlighted in bold.…”

Section: Comparison With State-of-the-art Approaches On Test Functionsmentioning

confidence: 99%

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Nseef

Abdullah

Turky

et al. 2016

Knowledge-Based Systems

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Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multipopulation artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC.Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.

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“…The time series forecasting is a relevant area of study, considering the numerous examples of time series in the literature, such as Souza (2008), Souza et al (2009), Cui et al (2015aCui et al ( , 2015b, Liu et al (2015) and Prema et al (2015). In order to be established future values for the series under study, it is necessary that, in some way, it can capture and formulate a mathematical model able to represent the behavior and the characteristics of time series that is wanted to be predict.…”

Section: Introductionmentioning

confidence: 99%

Monthly Closing Price Forecasting of Soybean Grain in Paraná Using Sarima Modeling With Intervention

Souza¹,

Filho²,

Santos³

et al. 2016

J. Geo. Mod.

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This article presents a procedure to find a suitable model for forecasting financial time series of agribusiness commodities considering the negotiation and circumstantial factors that influence them, especially seasonality and abrupt economic changes. The used data were the series of monthly closing prices of soybean in the state of Paraná between the months of February 1998 and January 2004. This series and its transformations were tested empirically in terms of ACF analysis (Auto-Correlation Function) until stationarized changes were found. For this, the application of seasonal adjustment was necessary due to the characteristic of the series and interventions (dummy variables) due to structural breaks occurred during the analyzed period. The seasonal adjustment was done through the X-12-ARIMA method, which implements the univariate ARIMA model (AutoRegressive Integrated Moving Average) and the multiplicative seasonal (SARIMA). The combined use of SARIMA model and of interventions got, in terms of the statistical analysis adopted, modeling the negotiation factors known to influence their oscillations and realize the forecast with 1, 2 and 3 steps ahead.

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A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction

Cited by 31 publications

References 47 publications

Multiplicative Neuron Model Based on Sine Cosine Algorithm for Time Series Prediction

Multiplicative Neuron Model Based on Sine Cosine Algorithm for Time Series Prediction

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Monthly Closing Price Forecasting of Soybean Grain in Paraná Using Sarima Modeling With Intervention

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