Long-term time series prediction with the NARX network: An empirical evaluation

Menezes, José Maria P.; Barreto, Guilherme A.

doi:10.1016/j.neucom.2008.01.030

Cited by 372 publications

(162 citation statements)

References 28 publications

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“…A nonlinear autoregressive exogenous (NARX) network is a complex discrete-time nonlinear system with feedback connections Menezes and Barreto (2008). For time series modeling, NARX utilizes current and past values together with nonlinear inputoutput mapping for dynamical prediction.…”

Section: Nonlinear Autoregressive Exogenous Networkmentioning

confidence: 99%

Neural-network-based modelling and analysis for time series prediction of ship motion

Kawan

Wang

et al. 2017

Ship Technology Research

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Abstract:This paper presents a data-driven model for time series prediction of ship motion. Prediction based on past time series of data is a powerful function in modern ship support systems. For a large amount of ship sensor data, neural network (NN) is considered as a proper tool in modeling the prediction system. Efforts are made to compact the NN structure through sensitive analysis, in which the importance of each input to the output is quantized and lower ranked inputs are eliminated. Further analysis about the impact of three different learning strategies, i.e., offline, online and hybrid learning on the NN is conducted. The hybrid learning combining the advantages of both the offline learning and the online learning exhibits superior prediction performance. According to the long term prediction ability of recurrent NN, multi-stepahead prediction under the hybrid learning strategy is realized in a multi-stage prediction form. Experiments are carried out using collected ship sensor data on a vessel. The results show the feasibility of generating a data-driven model through modeling and analysis of the NN for ship motion prediction.Additional Information: Question ResponseIs your article an invited contribution for a special issue? If yes, please select the title of the special issue from the list below.Please select an option ONLY if you have received an invitation to submit. ABSTRACT This paper presents a data-driven model for time series prediction of ship motion. Prediction based on past time series of data is a powerful function in modern ship support systems. For a large amount of ship sensor data, neural network (NN) is considered as a proper tool in modeling the prediction system. Efforts are made to compact the NN structure through sensitive analysis, in which the importance of each input to the output is quantized and lower ranked inputs are eliminated. Further analysis about the impact of three different learning strategies, i.e., offline, online and hybrid learning on the NN is conducted. The hybrid learning combining the advantages of both the offline learning and the online learning exhibits superior prediction performance. According to the long term prediction ability of recurrent NN, multi-step-ahead prediction under the hybrid learning strategy is realized in a multi-stage prediction form. Experiments are carried out using collected ship sensor data on a vessel. The results show the feasibility of generating a data-driven model through modeling and analysis of the NN for ship motion prediction. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation

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Section: Nonlinear Autoregressive Exogenous Networkmentioning

confidence: 99%

Neural-network-based modelling and analysis for time series prediction of ship motion

Kawan

Wang

et al. 2017

Ship Technology Research

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“…The feasibility of NARXnns as a nonlinear tool for time series modeling and prediction is key for carrying out long-term time series predictions [54].…”

Section: Narxnn and Its Configuration For Sm Retrievalmentioning

confidence: 99%

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

et al. 2017

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Abstract:A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

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“…Neural networks are often mentioned in the context of non linear systems [24,25]. NARX models are useful models to predict discrete dynamic time series.…”

Section: Neural Network For the Downscaling Taskmentioning

confidence: 99%

On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany

Kronenberg¹,

Barfus²,

Franke³

et al. 2013

ACS

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In this study, recurrent networks to downscale meteorological fields of the ERA-40 re-analysis dataset with focus on the meso-scale water balance were investigated. Therefore two types of recurrent neural networks were used. The first approach is a coupling between a recurrent neural network and a distributed watershed model and the second a nonlinear autoregressive with exogenous inputs (NARX) network, which directly predicted the component of the water balance. The approaches were deployed for a meso-scale catchment area in the Free State of Saxony, Germany. The results show that the coupled approach did not perform as well as the NARX network. But the meteorological output of the coupled approach already reaches an adequate quality. However the coupled model generates as input for the watershed model insufficient daily precipitation sums and not enough wet days were predicted. Hence the long-term annual cycle of the water balance could not be preserved with acceptable quality in contrary to the NARX approach. The residual storage change term indicates physical restrictions of the plausibility of the neural networks, whereas the physically based correlations among the components of the water balance were preserved more accurately by the coupled approach.

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Long-term time series prediction with the NARX network: An empirical evaluation

Cited by 372 publications

References 28 publications

Neural-network-based modelling and analysis for time series prediction of ship motion

Neural-network-based modelling and analysis for time series prediction of ship motion

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany

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