NON-STATIONARY TIME SERIES PREDICTION USING ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK MODELS

2020 ◽  
Vol 3 (1) ◽  
pp. 362-372
Author(s):  
Svitlana Antoshchuk ◽  
Oksana Babilunha ◽  
Thanh Tran Kim ◽  
Anatolii Nikolenko ◽  
Tien Nguyen Thi Khanh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Prediction ◽  
Network Models ◽  
Stationary Time Series ◽  
Neural Network Models ◽  
One Dimensional ◽  
Stationary Time

scholarly journals Evolutionary product unit based neural networks for hydrological time series analysis

2010 ◽  
Vol 13 (4) ◽  
pp. 825-841 ◽  
Author(s):  
Dulakshi S. K. Karunasingha ◽  
A. W. Jayawardena ◽  
W. K. Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Meteorological Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Engineering Analysis ◽  
Neural Network Models ◽  
Mlp Neural Network ◽  
Evolutionary Product

Artificial Neural Networks (ANNs) are now widely used in many areas of science, medicine, finance and engineering. Analysis and prediction of time series of hydrological/and meteorological data is one such application. Problems that still exist in the application of ANN's are the lack of transparency and the expertise needed for training. An evolutionary algorithm-based method to train a type of neural networks called Product Units Based Neural Networks (PUNN) has been proposed in a 2006 study. This study investigates the applicability of this type of neural networks to hydrological time series prediction. The technique, with a few small changes to improve the performance, is applied to some benchmark time series as well as to a real hydrological time series for prediction. The results show that evolutionary PUNN produce more transparent models compared to widely used multilayer perceptron (MLP) neural network models. It is also seen that training of PUNN models requires less expertise compared to MLPs.

scholarly journals On the prediction of solar activity using different neural network models

1996 ◽  
Vol 14 (1) ◽  
pp. 20-26 ◽  
Author(s):  
F. Fessant ◽  
S. Bengio ◽  
D. Collobert
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Solar Activity ◽  
Time Series Prediction ◽  
Network Models ◽  
Accurate Prediction ◽  
Experimental Results ◽  
Neural Network Models ◽  
Telecommunication Companies

Abstract. Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters rely strongly on solar activity. In this paper, we analyze the use of neural networks for sunspot time series prediction. Three types of models are tested and experimental results are reported for a particular sunspot time series: the IR5 index.

scholarly journals Time Series Traffic Speed Prediction Using k-Nearest Neighbour Based on Similar Traffic Data

2018 ◽  
Vol 218 ◽  
pp. 03021 ◽  
Author(s):  
Bagus Priambodo ◽  
Yuwan Jumaryadi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Historical Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Traffic Data ◽  
Data Traffic ◽  
Neural Network Models

During the past few years, time series models and neural network models are widely used to predict traffic flow and traffic congestion based on historical data. Historical data traffic from sensors is often applied to time series prediction or various neural network predictions. Recent research shows that traffic flow pattern will be different on weekdays and weekends. We conducted a time series prediction of traffic flow on Monday, using data on weekdays and whole days data. Prediction of short time traffic flows on Monday based on weekdays data using k-NN methods shows a better result, compared to prediction based on all day’s data. We compared the results of the experiment using k-NN and Neural Network methods. From this study, we observed that generally, using similar traffic data for time series prediction show a better result than using the whole data.

scholarly journals Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump

2021 ◽  
Vol 2095 (1) ◽  
pp. 012069
Author(s):  
Lishan Zhang ◽  
Lei Han ◽  
Yuzhen Meng ◽  
Wenkui Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Radial Vibration ◽  
Hydraulic Pump ◽  
Neural Network Models ◽  
Vibration Signals ◽  
One Dimensional ◽  
Fully Connected

Abstract Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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