A combination of artificial neural network and random walk models for financial time series forecasting

Adhikari, Ratnadip; Agrawal, R. K.

doi:10.1007/s00521-013-1386-y

Cited by 141 publications

(45 citation statements)

References 16 publications

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“…harr, db2, and db4. [1,14,20]. From Table 2, it can be clearly seen that Zhang's model has achieved lower errors than both ARIMA and ANN and that our DWT based hybrid approach has outperformed all the three models through producing least errors.…”

Section: Resultsmentioning

confidence: 90%

Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

Khandelwal

Adhikari

Verma

2015

Procedia Computer Science

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231

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Recently Discrete Wavelet Transform (DWT) has led to a tremendous surge in many domains of science and engineering. In this study, we present the advantage of DWT to improve time series forecasting precision. This article suggests a novel technique of forecasting by segregating a time series dataset into linear and nonlinear components through DWT. At first, DWT is used to decompose the in-sample training dataset of the time series into linear (detailed) and non-linear (approximate) parts. Then, the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models are used to separately recognize and predict the reconstructed detailed and approximate components, respectively. In this manner, the proposed approach tactically utilizes the unique strengths of DWT, ARIMA, and ANN to improve the forecasting accuracy. Our hybrid method is tested on four real-world time series and its forecasting results are compared with those of ARIMA, ANN, and Zhang's hybrid models. Results clearly show that the proposed method achieves best forecasting accuracies for each series.

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Section: Resultsmentioning

confidence: 90%

Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

Khandelwal

Adhikari

Verma

2015

Procedia Computer Science

Self Cite

231

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“…The ARIMA (0, 1, 0), i.e. y t − y t−1 = ε t is known as the random walk (RW) model and is by far the most dominating linear model for financial time series forecasting [2,3,42]. Many real-world time series often contain seasonal or cyclic components which need special treatments.…”

Section: The Box-jenkins Modelmentioning

confidence: 99%

A mutual association based nonlinear ensemble mechanism for time series forecasting

Adhikari

2015

Appl Intell

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Forecasting a time series with reasonable accuracy is an important but quite difficult task that has been attracting lots of research attention for many years. A widely approved fact is that combining forecasts from multiple models significantly improves the forecasting precision as well as often produces better forecasts than each constituent model. The existing literature is accumulated with linear methods of combining forecasts but nonlinear approaches have received very limited research attention, so far. This paper proposes a novel nonlinear forecasts combination mechanism in which the combined model is constructed from the individual forecasts and the mutual dependencies between pairs of forecasts. The individual forecasts are performed through three well recognized models, whereas five correlation measures are investigated for estimating the mutual association between two different forecasts. Empirical analysis with six real-world time series demonstrates that the proposed ensemble substantially reduces the forecasting errors and also outperforms each component model as well as other conventional linear combination methods, in terms of out-of-sample forecasting accuracy.

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“…It appears in many different areas like physical time series, economic time series and marketing and sales time series. From statistical to artificial intelligence, there is a range of neural network techniques which have been used to handle a time series problem [1,2,3,4]. Neural networks (NNs) have appeared as an effective tool for forecasting of time series [5].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Techniques for Time Series Prediction: A Review

Mushtaq

Akram

Aamir

et al. 2019

JOIV : Int. J. Inform. Visualization

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It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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A combination of artificial neural network and random walk models for financial time series forecasting

Cited by 141 publications

References 16 publications

Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

Time Series Forecasting Using Hybrid ARIMA and ANN Models Based on DWT Decomposition

A mutual association based nonlinear ensemble mechanism for time series forecasting

Neural Network Techniques for Time Series Prediction: A Review

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