Are neural networks able to forecast nonlinear time series with moving average components?

Cogollo, Myladis R.; Velásquez, Juan D.

doi:10.1109/tla.2015.7273790

Cited by 9 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…end for (20) Train the Rotation Forest in training set (21) for training set do (22) y � D (k) (F (k) ) (23) end for (24) Calculate y〈k〉 (25) for training and testing set do (26) y � D (k) (F (k) ) (27) end for (28) Calculate changes (29) s � MSE(y (k) , y (k) ) (30) until k > k m ax or s < eps (31) Construct F<k> (32) for j from 1 to k do (33) Calculate e (k) , F (k) , y (k) (34) end for (35)…”

Section: Discussion About Ma Termsmentioning

confidence: 99%

“…e main obstacle of such a combination of machine learning algorithms and the ARMA or ARIMA model is that the MA parts are basically the error terms between real and predicted observation, so it refers to the model result, which is still unknown when solving the model. us, the family of ARMA models is hard to be modeled by the ANN and other similar ML structures [32][33][34]. In the application of time series models to HI or RUL prediction, researchers mainly combine the ARMA family and ML methods as two independent parts, such as combining ARMA and SVM [35] and particle filter (PF) [23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Luo

Wang

Yang

2022

Shock and Vibration

View full text Add to dashboard Cite

A typical way to predict the remaining useful life (RUL) of bearings is to predict certain health indicators (HIs) according to the historical HI series and forecast the end of life (EOL). The autoregressive neural network (ARNN) is an early idea to combine the artificial neural network (ANN) and the autoregressive (AR) model for forecasting, but the model is limited to linear terms. To overcome the limitation, this paper proposes an improved autoregressive integrated moving average with the recurrent process (ARIMA-R) method. The proposed method adds moving average (MA) components to the framework of ARNN, adding the long-range dependence and nonlinear factors. To deal with the recursive characteristics of the MA term, a process of MA component estimating is constructed based on the expectation-maximum method. In the concrete realization of the method, the rotation tree (RTF) is introduced in place of ANN to improve the prediction performance. The experiment on FEMTO datasets reveals that the proposed ARIMA-R method outperforms the ARNN method in terms of predictive performance evaluation indicators.

show abstract

Section: Discussion About Ma Termsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Luo

Wang

Yang

2022

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…In order to adjust the weights and biases in neurons, BPNN employs error backpropagation operation. Benefiting from the gradient-descent feature, the algorithm has become an effective function approximation algorithm [20, 21]. A standard BPNN which consists of a number of m inputs and n outputs is shown in Figure 1.…”

Section: Algorithm Designmentioning

confidence: 99%

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

Liu

Jing

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning.

show abstract

A Comparative Study of Statistical and Neural Network Models for PLC Network Traffic Anomaly Detection

Andrysiak

Saganowski

2019

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Are neural networks able to forecast nonlinear time series with moving average components?

Cited by 9 publications

References 25 publications

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model

A Comparative Study of Statistical and Neural Network Models for PLC Network Traffic Anomaly Detection

Contact Info

Product

Resources

About