scholarly journals Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction

TEM Journal ◽  
2021 ◽  
pp. 1955-1963
Author(s):  
Ajla Kulaglic ◽  
B. Berk Ustundag
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Training Dataset ◽  
Lorenz Attractor ◽  
Predictive Error ◽  
Prediction Problems

Multivariable machine learning (ML) models are increasingly used for time series predictions. However, avoiding the overfitting and underfitting in ML-based time series prediction requires special consideration depending on the size and characteristics of the available training dataset. Predictive error compensating wavelet neural network (PEC-WNN) improves the time series prediction accuracy by enhancing the orthogonal features within a data fusion scheme. In this study, time series prediction performance of the PEC-WNNs have been evaluated on two different problems in comparison to conventional machine learning methods including the long short-term memory (LSTM) network. The results have shown that PECNET provides significantly more accurate predictions. RMSPE error is reduced by more than 60% with respect to other compared ML methods for Lorenz Attractor and wind speed prediction problems.

Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems

2018 ◽  
Vol 159 ◽  
pp. 132-147 ◽  
Author(s):  
Hongbing Wang ◽  
Zhengping Yang ◽  
Qi Yu ◽  
Tianjing Hong ◽  
Xin Lin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Term Memory ◽  
Service Oriented ◽  
Long Short Term Memory

scholarly journals The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 440 ◽  
Author(s):  
Moyang Liu ◽  
Yingchun Huang ◽  
Zhijia Li ◽  
Bingxing Tong ◽  
Zhentao Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Flood Forecasting ◽  
K Nearest Neighbor ◽  
Climate Zones ◽  
Climate Conditions ◽  
Prediction Problems

Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

scholarly journals Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 487 ◽  
Author(s):  
Trang Thi Kieu Tran ◽  
Taesam Lee ◽  
Ju-Young Shin ◽  
Jong-Suk Kim ◽  
Mohamad Kamruzzaman
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Human Life ◽  
Time Series Prediction ◽  
Vital Role ◽  
Maximum Temperature ◽  
Hyperparameter Optimization ◽  
Meta Learning

Time series forecasting of meteorological variables such as daily temperature has recently drawn considerable attention from researchers to address the limitations of traditional forecasting models. However, a middle-range (e.g., 5–20 days) forecasting is an extremely challenging task to get reliable forecasting results from a dynamical weather model. Nevertheless, it is challenging to develop and select an accurate time-series prediction model because it involves training various distinct models to find the best among them. In addition, selecting an optimum topology for the selected models is important too. The accurate forecasting of maximum temperature plays a vital role in human life as well as many sectors such as agriculture and industry. The increase in temperature will deteriorate the highland urban heat, especially in summer, and have a significant influence on people’s health. We applied meta-learning principles to optimize the deep learning network structure for hyperparameter optimization. In particular, the genetic algorithm (GA) for meta-learning was used to select the optimum architecture for the network used. The dataset was used to train and test three different models, namely the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Our results demonstrate that the hybrid model of an LSTM network and GA outperforms other models for the long lead time forecasting. Specifically, LSTM forecasts have superiority over RNN and ANN for 15-day-ahead in summer with the root mean square error (RMSE) value of 2.719 (°C).

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 2636-2640 ◽  
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu ◽  
Ke Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Agricultural Production ◽  
Supply And Demand ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Agricultural Products ◽  
Price Model ◽  
Agricultural Prices

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

Time-series prediction using a local linear wavelet neural network

2006 ◽  
Vol 69 (4-6) ◽  
pp. 449-465 ◽  
Author(s):  
Yuehui Chen ◽  
Bo Yang ◽  
Jiwen Dong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Local Linear

TIME SERIES PREDICTION OF WAVE HEIGHT BY LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORK

2021 ◽  
Vol 77 (2) ◽  
pp. I_151-I_156
Author(s):  
Nagisa SUMITANI ◽  
Tomohiro YASUDA ◽  
Nobuhito MORI ◽  
Tomoya SHIMURA
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wave Height ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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