scholarly journals SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING

2020 ◽  
Vol 13 (2) ◽  
pp. 116-124
Author(s):  
Hermansah Hermansah ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Input Variables ◽  
Nonlinear Autoregressive ◽  
Selection Of ◽  
Autoregressive Neural Network

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.

scholarly journals Bayesian Stochastic Neural Network Model for Turbomachinery Damage Prediction

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shuhua Yang ◽  
Xiaomo Jiang ◽  
Shengli Xu ◽  
Xiaofang Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Signal Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Stochastic Neural Network ◽  
Damage Prediction ◽  
Multivariate Correlation

Turbomachinery often suffers various defects such as impeller cracking, resulting in forced outage, increased maintenance costs, and reduced productivity. Condition monitoring and damage prognostics has been widely used as an increasingly important and powerful tool to improve the system availability, reliability, performance, and maintainability, but still very challenging due to multiple sources of data uncertainties and the complexity of analytics modeling. This paper presents an intelligent probabilistic methodology for anomaly prediction of high-fidelity turbomachine, considering multiple data imperfections and multivariate correlation. The proposed method adeptly integrates several advanced state-of-the-art signal processing and artificial intelligence techniques: wavelet multi-resolution decomposition, Bayesian hypothesis testing, probabilistic principal component analysis, and fuzzy stochastic neural network modeling. The advanced signal processing is employed to reduce dimensionality and to address multivariate correlation and data uncertainty for damage prediction. Instead of conventionally using raw time series data, principal components are utilized in the establishment of stochastic neural network model and anomaly prediction. Bayesian interval hypothesis testing metric is then presented to quantitatively compare the predicted and measured data for model validation and anomaly evaluation, thus providing a confidence indicator to judge the model quality and evaluate the equipment status. Bayesian method is developed in this study for denoising the raw data with multiresolution wavelet decomposition, quantifying the model accuracy, and assessing the equipment status. The dynamic stochastic neural network model is established via the nonlinear autoregressive moving average with exogenous inputs approach. It seamlessly integrates the fuzzy clustering and independent Bernoulli random function into radial basis function neural network. A natural gradient method based on Kullback-Leibler distance criterion is employed to maximize the log-likelihood loss function. The effectiveness of the proposed methodology and procedure is demonstrated with the 11-variable time series data and the forced outage event of a real-world centrifugal compressor.

scholarly journals Automatic time series forecasting using nonlinear autoregressive neural network model with exogenous input

2021 ◽  
Vol 10 (5) ◽  
pp. 2836-2844
Author(s):  
Hermansah Hermansah ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Mean Squared Error ◽  
Real Data ◽  
Hidden Neurons ◽  
Nonlinear Autoregressive ◽  
Automatic Forecasting ◽  
Autoregressive Neural Network

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm sets up the appropriate features, estimate the parameters in the model, and calculate forecasts, without the users’ intervention. The algorithm method used include preprocessing, tests for trends, and the application of first differences. The time series were tested for seasonality, and seasonal differences were obtained from a successful analysis. These series were also linearly scaled to [−1, +1]. The autoregressive lags and hidden neurons were further selected through the stepwise and optimization algorithms, respectively. The 20 NARX models were fitted with different random starting weights, and the forecasts were combined using the ensemble operator, in order to obtain the final product. This proposed method was applied to real data, and its performance was compared with several available automatic models in the literature. The forecasting accuracy was also measured by mean squared error (MSE) and mean absolute percent error (MAPE), and the results showed that the proposed method outperformed the other automatic models.

Determination of input nodes for neural network model in time series data

2020 ◽  
Vol 22 (3) ◽  
pp. 1055-1065
Author(s):  
Hyeonjung Park ◽  
Seungbae Choi ◽  
Changwan Kang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data

scholarly journals Proposal and Evaluation of Pavement Deterioration Prediction Method by Recurrent Neural Network

2017 ◽  
Vol 3 (4) ◽  
pp. 16
Author(s):  
Tomoyuki Okuda ◽  
Kouyu Suzuki ◽  
Naohiko Kohtake
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Survey Data ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Series Data ◽  
Pavement Deterioration

The pavement deterioration prediction model is a basic module of the PMS (Pavement Management System), and its prediction results influence the decision making of pavement administrators. Hence, it is very important to improve prediction accuracy. There is a rutting depth as a major indicator showing the state of pavement used in PMS. In this research, we propose a method to predict rutting depth by introducing a technique to improve prediction accuracy by suppressing over-fitting by dropout and gradient clipping in the recently rapidly developing NN (Neural Network) model. In addition, since pavement survey data are time series data that were inspected at the same place multiple times, we applied the RNN (Recurrent Neural Network) model which can model time series data. The proposed method was applied to rutting depth prediction of periodic survey data of Kawasaki city in Japan from 1987 to 2016. In order to compare prediction accuracy, we predicted three years later using proposed method and MLR (Multiple Linear Regression) which is a typical regression model and MLP (Multi-Layer Perceptron) which is most frequently used among NN models. The RMSE and correlation coefficient R between the prediction result and the measured value were compared. We validated that the prediction ability of RNN is the highest.

scholarly journals PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (Studi Kasus :PT Bumi Serpong Damai Tbk. Dan PT H.M Sampoerna Tbk.)

2018 ◽  
Vol 7 (2) ◽  
pp. 200-211
Author(s):  
Rizki Pradipto Widyantomo ◽  
Abdul Hoyyi ◽  
Tatik Widiharih
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Arima Model ◽  
Garch Model ◽  
Residual Variance ◽  
Series Analysis ◽  
Input Variables

Time series analysis is an analysis used to predict a time-observed data, one of which is the ARIMA model. ARIMA model assumes a constant residual variance (homogeneous). While financial data usually produce ARIMA model with variance error that is not constant. If the assumption of homogeneity of the residual variance is not met, then the method that can be used is ARCH or GARCH model. Another method that can be used on the data assuming the homogeneity of the variance error is not met is the Neural Network model. In this model we use Neural Network model with variance and residual as the input variables that obtained from ARCH / GARCH model. The data used are BSDE and HMSP asset portfolio returns from November 14, 2016 to January 18, 2018. In this study the selected input variables are from ARIMA (1.0.1) GARCH (1,1) model. The best Neural Network model obtained is Neural Network model with 10 hidden layers with MSE value 6.58 x10-10 with model train evaluation which is MAPE value 1.14441%.Keywords: Time series Analysis, ARCH / GARCH, Neural Network, Return.

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