A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

Babu, C. Narendra; Reddy, B. Eswara

doi:10.1016/j.asoc.2014.05.028

Cited by 423 publications

(215 citation statements)

References 22 publications

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“…Shukur and Lee [11] stated that the non-linearity in the patterns of wind speed data was the reason for inaccurate wind speed forecasting using a linear ARIMA model, and the inaccurate forecasting of the ARIMA model reflected the uncertainty of the modelling process. Babu and Reddy [12] explored both linear ARIMA and non-linear artificial neural network (ANN) models to devise a new hybrid ARIMA-ANN model for the forecasting of electricity price. Cadenas and Rivera [13] also combined ARIMA and ANN for wind speed forecasting using measured hourly wind speed time series at different sites during one month.…”

Section: Statistical Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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Section: Statistical Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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“…The study of the correlation between soil nutrients can reveal the relationship between the soil properties and the coordination effect between the indicators. Regression analysis is based on the change of the number of independent variables to predict the dependent variable, it is the regression equation as a prediction model [2] . This chapter mainly focused on the correlation between the yield and time series of soil properties in the precision work area, and established the model of the relationship between crop yield and time.…”

Section: Methodsmentioning

confidence: 99%

The Time Variation of the Maize Yield in Precise Operation Section

Zhao¹,

Han²,

Cao³

et al. 2016

Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

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Abstract. In this paper, a variety of curve estimation method is used to predict the time series characteristics of Maize Yield in more than 20 years, and it is found that each prediction method has its applicable conditions and applicable scope. Comparison of the parameters of the estimated parameters of the four kinds of curves found that the effect of the three curve estimation is optimal, so the function of the three Cubic is more appropriate to observe the value of the fitting. If the ARIMA model is used to predict the yield of maize, the AIC (2, 1, 1) (ARIMA) has achieved good results in the study. The conclusions can provide some guidance and practical significance for the current and even future production management decision.

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“…The SVM worse performed when increase data-set. Babu and Reddy (2014) proposed autoregressive integrated moving average-ANN to predict stock-market price. The ARIMA-ANN model works on the bases of one-step-ahead and multistep-ahead prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Now a day prediction/forecasting are important and interesting research area. Prediction used (Babu and Reddy, 2014) in different application such as internet-traffic facilities service providers for enhancing their service, prediction temperature, weather and change in environment-gives facilities to formals or agricultures sectors, prediction disasters such as earth-quick, flood etc. The prediction is important activity in stock market which gives information to investor for safely investment in the stock market.…”

Section: Introductionmentioning

confidence: 99%

Pakistan stock exchange prediction using RIDOR classifier

Akram¹,

Imran

2017

Int. j. adv. appl. sci.

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Now-a-days stock market prediction is important activity and interesting topic for professional analyst. The stock market is biggest investment platform, however investment in stock market need accurate and complete information. The accurate and fast-prediction of stock market attracted the investor for profitable output. The stock market prediction is complex task because uncertainty involves in the market movement up/down. Mostly the machine learning techniques (MLT) are used for accurate prediction of stock market, because of its capability of partitioning; extract hidden information form raw data, monitors the fluctuation rate of stock market, suitable for nonlinear data etc. This research work is about to review the strength and weakness of existing stock market prediction techniques. This research work proposed a Ripple-down-rule-learner (RIDOR) classifier based technique. The RIDOR rule base classifier generates default value and work like if-else statement for uncertainties. The other contribution is a prepared data-set using technical indicator to predict stock market trend. The output of the propose model is outperformed as compare to the exiting techniques.

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A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

Cited by 423 publications

References 22 publications

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

The Time Variation of the Maize Yield in Precise Operation Section

Pakistan stock exchange prediction using RIDOR classifier

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