An improved combination approach based on Adaboost algorithm for wind speed time series forecasting

Xiao, Litian; Dong, Yunxuan; Dong, Yao

doi:10.1016/j.enconman.2018.01.038

Cited by 68 publications

(23 citation statements)

References 34 publications

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“…False, otherwise (26) where η T and η V are the average prediction errors on the training set and the validation set respectively, which can be obtained by…”

Section: Over-fitting Prevention Ensemble Predictor 1) Long Short-mentioning

confidence: 99%

“…However, multivariate time series usually contains multiple subsequences with strongly nonlinear fluctuations, making it hard to obtain ideal prediction performance. To address this issue, many researches [24]- [26] apply the AdaBoost algorithm to combine a series of weak predictors to obtain a strong predictor, thus improving prediction accuracy. Nevertheless, these AdaBoost methods suffer from the over-fitting problem when used for time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

et al. 2019

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Multivariate time series forecasting recently has received extensive attention with its wide application in finance, transportation, environment, and so on. However, few of the currently developed models have considered the impact of noise on prediction. Since multivariate time series contains multiple subsequences with strong nonlinear fluctuations, it is also difficult to obtain satisfactory prediction results. In this paper, aiming at improving prediction performance, we have proposed a novel ensemble threephase model called adaptive noise reducer-stacked auto-encoder-validating-AdaBoost-based long shortterm memory (ANR-SAE-VALSTM). We start with an introduction of a novel ANR for time series noise elimination. The SAEs are then used to extract features from the de-noised multivariate time series. Finally, we feed the de-noised features into the VALSTM to train an ensemble over-fitting prevention predictor. The proposed model is employed on the Beijing PM2.5 dataset and GEFCom2014 Electricity Price dataset. Compared with other popular models, the proposed model has achieved the best prediction performance in all prediction horizons. In addition, a careful ablation study is conducted to demonstrate the efficiency of our model design. INDEX TERMS Multivariate time series forecasting, adaptive noise reducer, stacked auto-encoders, long short-term memory, validating AdaBoost algorithm.

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“…False, otherwise (26) where η T and η V are the average prediction errors on the training set and the validation set respectively, which can be obtained by…”

Section: Over-fitting Prevention Ensemble Predictor 1) Long Short-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

et al. 2019

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“…Numerous variants of ANN, such as Back Propagation Neural Network (BPNN) [4, 5], Radial Basis Function Neural Network (RBFNN) [6], Generalized Regression Neural Network (GRNN) [7] and Wavelet Neural Network (WNN) [8, 9], were also proposed to predict the future wind energy. Besides, some scholars utilized Support Vector Machine (SVM) [10] or its variants [11, 12] to forecast wind energy. Deep learning approaches, such as Autoencoder (AE), Deep Boltzmann Machine (DBM) [13], Convolutional Neural Network (CNN) [14], Recurrent Neural Networks (RNNs) [15] and so on, were also adopted to forecast the future wind energy.…”

Section: Introductionmentioning

confidence: 99%

A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China

Zhang

She

2019

PLoS ONE

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Wind energy is one of the most important renewable resources and plays a vital role in reducing carbon emission and solving global warming problem. Every country has made a corresponding energy policy to stimulate wind energy industry development based on wind energy production, consumption, and distribution. In this paper, we focus on forecasting wind energy consumption from a macro perspective. A novel power-driven fractional accumulated grey model (PFAGM) is proposed to solve the wind energy consumption prediction problem with historic annual consumption of the past ten years. PFAGM model optimizes the grey input of the classic fractional grey model with an exponential term of time. For boosting prediction performance, a heuristic intelligent algorithm WOA is used to search the optimal order of PFAGM model. Its linear parameters are estimated by using the least-square method. Then validation experiments on real-life data sets have been conducted to verify the superior prediction accuracy of PFAGM model compared with other three well-known grey models. Finally, the PFAGM model is applied to predict China’s wind energy consumption in the next three years.

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“…It is used to train multi‐weak learners, and assign weights to each weak learner, then boost multi‐weak learners into a strong one with abilities of high generalisation and non‐linear reflecting, which can eliminate the over‐fitting phenomenon effectively [11, 12]. Nowadays, the algorithm is widely employed for face recognition, short‐term wind speed prediction, furnace temperature, fault diagnosis, and others [13–17].…”

Section: Introductionmentioning

confidence: 99%

Research on low‐speed performance of continuous rotary electro‐hydraulic servo motor based on robust control with Adaboost prediction

Jing

Zhen

Sun

et al. 2018

J. eng.

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An improved combination approach based on Adaboost algorithm for wind speed time series forecasting

Cited by 68 publications

References 34 publications

An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting

A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China

Research on low‐speed performance of continuous rotary electro‐hydraulic servo motor based on robust control with Adaboost prediction

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