A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

2022 ◽  
Author(s):  
Md. Mijanur Rahman ◽  
Mohammad Shakeri ◽  
Fatema Khatun ◽  
Sieh Kiong Tiong ◽  
Ammar Ahmed Alkahtani ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Performance Analysis ◽  
Deep Neural Network ◽  
Time Series Forecasting ◽  
And Performance ◽  
Comprehensive Study

scholarly journals Determining the Rolling Window Size of Deep Neural Network Based Models on Time Series Forecasting

2021 ◽  
Vol 2078 (1) ◽  
pp. 012011
Author(s):  
Li Shen ◽  
Zijin Wei ◽  
Yangzhu Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gaussian Noise ◽  
Deep Neural Network ◽  
Window Size ◽  
Time Series Forecasting ◽  
Time Series Models ◽  
Arima Models ◽  
Information Theoretic ◽  
Rolling Window

Abstract Time series forecasting has always been a significant task in various domains. In this paper, we propose DeepARMA, a LSTM-based recurrent neural network to tackle this problem. DeepARMA is derived from an existing time series forecasting baseline, DeepAR, overcoming two of its weaknesses: (1) rolling window size determination: the way DeepAR determines rolling window size is casual and vulnerable, which may lead to the unnecessary computation and inefficiency of the model;(2) neglect of the noise: pure autoregressive model cannot deal with the condition where data are composed of various kinds of noise, neither do most of time series models including DeepAR. In order to solve these two problems, we first combine a classic information theoretic criterion, AIC, with the network to determine the proper rolling window size. Then, we propose a jointly-learned neural network fusing white Gaussian noise series given by ARIMA models to DeepAR’s input. That is exactly why we name the network ‘DeepARMA’. Our experiments on a real-world dataset demonstrate that our improvement settles those two problems put forward above.

HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting

2021 ◽  
pp. 717-727
Author(s):  
M. J. Jiménez-Navarro ◽  
F. Martínez-Alvarez ◽  
A. Troncoso ◽  
G. Asencio-Cortés
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Neural Network ◽  
Time Series Forecasting

scholarly journals Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

2014 ◽  
Vol 41 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Eren Bas
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Linear Time ◽  
Time Series Forecasting ◽  
Non Linear ◽  
Artificial Neural
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