A neural network based linear ensemble framework for time series forecasting

Adhikari, Ratnadip

doi:10.1016/j.neucom.2015.01.012

Cited by 114 publications

(62 citation statements)

References 44 publications

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“…Similarly, Adhikari optimized ARIMA with FANN, EANN and SVM to predict eight-time series familiar data sets in stock exchange price's prediction; this study achieved significantly better accuracy than each single component model. Moreover, variety of neural networks can be utilized as well non-linear algorithms [23]. Moreover, Khashei et al proposed method that combined ARIMA methods and PNN algorithm [24].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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Abstract-Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study is to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated, namely, ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model is examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

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Section: Related Workmentioning

confidence: 99%

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…The inclusion of the grey prediction model [19] of the rolling mechanism made the timeliness of the data series reflect more clearly. The inear neural network model [21] is a new linear prediction method that uses neural networks to determine model weights. The ARIMA model [20] can reflect the characteristics of a data sequence in self-similarity, periodicity and suddenness.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Yang

2018

Sustainability

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Construction of new coal-fired power plants in China has posed a huge challenge to energy sustainability. Forecasting the installed capacity more accurately can serve to develop better energy sustainability strategy. A comparison between linear and non-linear forecasting models can more comprehensively describe the characteristics of the prediction data and provide multi-angle analysis of the prediction results. In this paper, we develop four time-series forecasting techniques-metabolism grey model (MGM), autoregressive integrated moving average (ARIMA), grey model (GM)-ARIAM, and nonlinear metabolism grey model (NMGM)-for better forecasting of coal-fired power installed capacity. The average relative errors between the simulation and actual data of the MGM, GM-ARIMA, ARIMA, and NMGM model are 3.37%, 2.13%, 3.71% and 2.36% respectively, which indicate those four models can produce highly accurate results. The forecasting results show the average annual growth rate of China's coal-fired power installed capacity in the next ten years (2017-2016) will be 5.26% a year, which is slower than the average annual growth rate (8.20% a year) for 2007-2016. However, the average annual new added installed capacity for 2017-2026 will be 74 gigawatts, which is higher than the average annual added installed capacity (56 gigawatts) for 2007-2016.

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“…It a non-linear prediction model, achieving a stable prediction effect through determining the combining weights [40,41]. Researchers improved the network based on particle swarm optimization and an optimized genetic algorithm to make it ideal for many application scenarios [42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries' energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India's energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995-2016, India's energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

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A neural network based linear ensemble framework for time series forecasting

Cited by 114 publications

References 44 publications

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Forecasting China’s Coal Power Installed Capacity: A Comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

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