A Novel Weighted Ensemble Technique for Time Series Forecasting

Adhikari, Ratnadip; Agrawal, R. K.

doi:10.1007/978-3-642-30217-6_4

Cited by 25 publications

(28 citation statements)

References 13 publications

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“…Such as, Adhikari et al established a nonlinear-weightedensemble method that considers both the separate forecasts besides the correlation between pairs of forecasts. Their structure could offer practically improved forecasting accuracies for three general time-series data sets [22]. 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.…”

Section: Related Workmentioning

confidence: 86%

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: 86%

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

Khairalla¹,

XuNing²,

AL-Jallad³

2017

ijacsa

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“…Minimizing the SSE with respect to w gives the desired weight vector as w = F + Y, where F + is the MoorePenrose pseudo-inverse of F [1,17]. It is known that F + = F T F −1 F T , if the symmetric matrix F T F is nonsingular; otherwise F + can be computed from the singular value decomposition of F [17,19].…”

Section: Ordinary Least Square Methodsmentioning

confidence: 99%

“…This may be primarily due to the lack of well-defined theoretical settings or success records [29]. Few years ago, Freitas and Rodrigues [17] provided some useful insights in this direction and inspired by their concept, Adhikari and Agrawal [1] proposed a nonlinear forecasts combination technique. In their approach, the relationships among the participating models are measured through introducing nonlinear terms which are analogous to the linear correlations among the forecast pairs [1].…”

Section: Introductionmentioning

confidence: 98%

“…Few years ago, Freitas and Rodrigues [17] provided some useful insights in this direction and inspired by their concept, Adhikari and Agrawal [1] proposed a nonlinear forecasts combination technique. In their approach, the relationships among the participating models are measured through introducing nonlinear terms which are analogous to the linear correlations among the forecast pairs [1]. However, the use of linear correlations restricts the detection of any inherent nonlinear relationships between a pair of forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…Mean Absolute Error (MAE), Mean Squared Error (MSE), and Average Relative Variance (ARV). The results of the proposed nonlinear ensemble are also compared with those of five well-known linear forecasts combination techniques as well as the basic nonlinear forecasts ensemble of Adhikari and Agrawal [1].…”

Section: Introductionmentioning

confidence: 99%

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A mutual association based nonlinear ensemble mechanism for time series forecasting

Adhikari

2015

Appl Intell

Self Cite

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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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Near‐Earth heliospheric magnetic field intensity since 1750: 1. Sunspot and geomagnetic reconstructions

Owens

Cliver

McCracken³

et al. 2016

JGR Space Physics

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We present two separate time series of the near‐Earth heliospheric magnetic field strength (B) based on geomagnetic data and sunspot number (SSN). The geomagnetic‐based B series from 1845 to 2013 is a weighted composite of two series that employ the interdiurnal variability index; this series is highly correlated with in situ spacecraft measurements of B (correlation coefficient, r = 0.94; mean square error, MSE = 0.16 nT2). The SSN‐based estimate of B, from 1750 to 2013, is a weighted composite of eight time series derived from two separate reconstruction methods applied to four different SSN time series, allowing determination of the uncertainty from both the underlying sunspot records and the B reconstruction methods. The SSN‐based composite is highly correlated with direct spacecraft measurements of B and with the composite geomagnetic B time series from 1845 to 2013 (r = 0.91; MSE = 0.24 nT2), demonstrating that B can accurately reconstructed by both geomagnetic and sunspot‐based methods. The composite sunspot and geomagnetic B time series, with uncertainties, are provided as supporting information.

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A Novel Weighted Ensemble Technique for Time Series Forecasting

Cited by 25 publications

References 13 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

A mutual association based nonlinear ensemble mechanism for time series forecasting

Near‐Earth heliospheric magnetic field intensity since 1750: 1. Sunspot and geomagnetic reconstructions

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