scholarly journals A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 610 ◽  
Author(s):  
Xinghan Xu ◽  
Weijie Ren
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Back Propagation ◽  
Research Field ◽  
Chaotic Time Series ◽  
Good Prospect ◽  
Decomposition Approach ◽  
Model Based ◽  
Mode Decomposition

The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

scholarly journals A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Bp Neural Network ◽  
Firefly Algorithm ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Earth’S Climate ◽  
Bp Model

The change of the number of sunspots has a great impact on the Earth’s climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots. Secondly, the firefly algorithm is introduced to initialize the weights and thresholds of the BP neural network, and a prediction model is established for each IMF. Finally, the predicted values of these components are calculated to obtain the final predict results. Comparing BP model, FA-BP model, EMD-BP model, and VMD-BP model, the simulation results show that the proposed algorithm has higher prediction accuracy and can be used to forecast the time series of sunspots.

scholarly journals A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 177 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Monthly Precipitation ◽  
Variational Mode Decomposition ◽  
Precipitation Time Series ◽  
Mode Decomposition ◽  
Learning Machine

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model

2014 ◽  
Vol 974 ◽  
pp. 310-317 ◽  
Author(s):  
Jing Wen Zheng ◽  
Shi Xiao Li ◽  
Yang Kun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Crude Oil ◽  
Hybrid Model ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Mode Decomposition

Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the technique to decompose crude oil prices. Then we use Dynamic Artificial Neural Network (DAN2) and Back Propagation (BP) Neural Network separately as the technique to predict the deposed time series, and finally integrate the predictions produced by DAN2 or BP by Adaptive Linear Neural Network (ALNN) as the final result of predictions. EEMD has been proved as a very useful method to decompose the nonlinear and non-stationary time series, and DAN2, different from traditional artificial neural networks, also has obvious advantages over traditional ones. In this paper, EEMD and DAN2 are used to predict crude oil prices at the first time。 All in all, we build four models-EEMD-DAN2-ALNN, EMD-BP-ALNN, EEMD-BP-ALNN and EMD-DAN2-ALNN to test which technique, EMD or EEMD, could do better job in decomposition of crude oil prices in this kind of hybrid model and whetherDAN2 could outshine BP when used in this hybrid model. Experimental results of four hybrid models indicate EEMD-DAN2-ALNN could gives the most precise predictions of crude oil prices, and DAN2 has a better performance than traditional neural networks-BP,when used in this hybrid model and EEMD could do a better job than EMD in decomposition of crude oil prices to yield precise predictions of crude oil prices in this model.

A hybrid model based on time series models and neural network for forecasting wind speed in the Brazilian northeast region

2018 ◽  
Vol 28 ◽  
pp. 65-72 ◽  
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Bosco Verçosa Leal Junior ◽  
Paulo Cesar Marques de Carvalho
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Time Series Models ◽  
Northeast Region ◽  
Model Based ◽  
Brazilian Northeast

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

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