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Temporal fusion transformer using variational mode decomposition for wind power forecasting
[article]
2023
arXiv
pre-print
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This study uses variational mode decomposition (VMD) to decompose the wind power series and Temporal fusion transformer (TFT) to forecast wind power for the next 1h, 3h and 6h. ...
The power output of a wind turbine depends on a variety of factors, including wind speed at different heights, wind direction, temperature and turbine properties. ...
TFT TFT is a deep neural network architecture based on the attention mechanism proposed by Lim et al. and is widely used in time series data prediction [31] . ...
arXiv:2302.01222v2
fatcat:d6wnycns4ncmfd56atw5irsmjy
A Hybrid Method with Adaptive Sub-series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting
2020
IEEE Access
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Besides, a multi-level attention mechanism (MLAttn), which makes full use of the encoding information of the encoder, has been introduced to further improve the prediction performance of the model. ...
In the prediction stage, the sub-series and correlation series will be fed into SRLSTMs-MLAttn for sub-series prediction. ...
NOTATIONS Univariate time series is a sequence of observations with continuous timestamps for a variable over a period of time. ...
doi:10.1109/access.2020.2981506
fatcat:xcbtnlcysjgevfcutchykieo4a
CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data
[article]
2023
arXiv
pre-print
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Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the predictive accuracy of TSF. ...
The proposed neural architecture was evaluated through comparative experiments with previous studies using five univariate time-series datasets and four multivariate time-series data. ...
Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A2C1102294). ...
arXiv:2211.16653v2
fatcat:lmzhcbok55elpjwafzpvom6fie
Multi-Step Short-Term Wind Speed Prediction using a Residual Dilated Causal Convolutional Network with Nonlinear Attention
2020
Energies
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In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). ...
Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en13071772
fatcat:43aesqap65fcjdwapyg2ptcm4i
A Hybrid Model for China's Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention
2022
Sustainability
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K-means clustering and reconstruction are applied to the components before being input to the CNN-GRU-Attention network for prediction to improve the model ability and adaptability of the sequences. ...
Therefore, a hybrid prediction model that combines component clustering and a neural network with an attention mechanism has been developed. ...
Existing research demonstrates that deep learning neural network models offer great benefits for handling time series data with high noise and high disorder [34] . ...
doi:10.3390/su142315522
fatcat:tswaiodmjzda5fmwu5uzl736y4
Hybrid Anomaly Detection via Multihead Dynamic Graph Attention Networks for Multivariate Time Series
2022
IEEE Access
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In this paper, to achieve improved anomaly detection performance for multivariate time series, we propose a novel architecture based on a graph attention network (GAT) with multihead dynamic attention ...
INDEX TERMS Multivariate time series, graph attention network, anomaly detection, deep generative model, gated recurrent unit. ...
Time series can be divided into univariate time series and multivariate time series. ...
doi:10.1109/access.2022.3167640
fatcat:osk4fwho3fchdhntuv6dyf7arm
Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms
2022
Electronics
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outperformed other models in terms of time series predictive performance. ...
Multi-variable time series (MTS) information is a typical type of data inference in the real world. ...
As disputed to univariate (solitary time series data) predicting, multi-variate time series analysis is frequently necessary for large statistics of linked time series data. ...
doi:10.3390/electronics11193167
fatcat:tjc2irmuerd4zkps5vkvvnjgje
A Look-Ahead Method for Forecasting the Concrete Price
2022
Journal of Applied Mathematics and Physics
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) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal rules of time series, to achieve accurate prediction of the trend of concrete price changes 10 days ago. ...
In this paper, a univariate autoregressive series is constructed based on the daily average price of concrete in major cities in China; then it uses a combined model of Convolutional Neural Network (CNN ...
Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper. ...
doi:10.4236/jamp.2022.105127
fatcat:uj6rcdwqqrajnlv3vvqylpdvk4
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
[article]
2021
arXiv
pre-print
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In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting. ...
Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the time domain and resort to pre-defined priors as inter-series ...
For example, FC-LSTM [36] forecasts univariate time-series with LSTM and fully-connected layers. ...
arXiv:2103.07719v1
fatcat:ysqgbsalfjehpojx22d2ae77gm
A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment
2019
Energies
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Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. ...
The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE). ...
By using an LSTMbased method to exploit the long-term dependencies of electric load time series, the prediction accuracy of load forecasting is improved [17] . ...
doi:10.3390/en12244612
fatcat:uuugqhhdqfgk3o7kmzxd4yjm2e
Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition
2022
Sensors
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In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict ...
Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. ...
Acknowledgments: We would like to thank the company Emececuadrado for their support for the realization of this work by providing the data that were processed. ...
doi:10.3390/s22103664
pmid:35632071
pmcid:PMC9145418
fatcat:rpywzvtgqva2pl2swd2aj2efpu
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
[article]
2022
arXiv
pre-print
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A graph neural network (GNN) architecture was used to extract spatial dependencies, with different update functions to learn temporal correlations. ...
This is the first time the LogSparse Transformer and Autoformer have been applied to wind forecasting and the first time any of these or the Informer have been formulated in a spatio-temporal setting for ...
The decomposition of a wind speed time-series is shown in Fig. ...
arXiv:2208.13585v1
fatcat:xhnnljuignfwzmbvgjh7gcai4q
Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model
2021
Remote Sensing
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This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide ...
The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction. ...
Thus, we incorporate an Attention Mechanism with an LSTM neural network to capture significant variation and improve the model's performance. ...
doi:10.3390/rs13061055
fatcat:4pg436i4xjel7ip6vxgqgrbyye
Time Series Analysis Based on Informer Algorithms: A Survey
2023
Symmetry
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The informer algorithm model performs relatively well on various data sets and has become a more typical algorithm model for time series forecasting, and its model value is worthy of in-depth exploration ...
Researchers have made significant improvements to the attention mechanism and Informer algorithm model architecture in these different neural network models, resulting in recent approaches such as wavelet ...
[12] improves the predictive power of models such as Informer for time series prediction. ...
doi:10.3390/sym15040951
fatcat:pqarxch5tnfj3cgog7mdv7ouuu
Landslide Risk Prediction Model Using an Attention-based Temporal Convolutional Network Connected to a Recurrent Neural Network
2022
IEEE Access
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Attention mechanismbased (Attn) temporal convolutional networks (TCN) connected with recurrent neural network (RNN) models for landslide risk prediction are proposed, including TCN-Attn-RNN and RNN-Attn-TCN ...
The encoder in the first model uses the temporal convolutional network (TCN), and the decoder uses a neural network with an RNN architecture, including long shortterm memory (LSTM) networks, gated recurrent ...
ACKNOWLEDGMENT The authors are deeply grateful to all the graduate students of the National and Local Joint Engineering Laboratory of Disaster Monitoring Technology and their help in the geological disaster ...
doi:10.1109/access.2022.3165051
fatcat:ky2ay5wzsrasvesyroipomjy6y
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