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Time Series Forecasting With Deep Learning: A Survey [article]

Bryan Lim, Stefan Zohren
<span title="2020-09-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time series forecasting -- describing how temporal information is incorporated into predictions  ...  Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category.  ...  (a) Non-probabilistic Hybrid Models With parametric time series models, forecasting equations are typically defined analytically and provide point forecasts for future targets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.13408v2">arXiv:2004.13408v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/escho2w6kvcmpin7umnwzpdahu">fatcat:escho2w6kvcmpin7umnwzpdahu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930010945/https://arxiv.org/pdf/2004.13408v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.13408v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting [article]

Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash
<span title="2022-02-25">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures.  ...  It integrates the knowledge and uncertainty from each data view in a dynamic context-specific manner assigning more importance to useful views to model a well-calibrated forecast distribution.  ...  datapoints to model a flexible non-parametric distribution for univariate sequences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.07438v3">arXiv:2109.07438v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/evrjyb2jqjc65kelr3w7wj4phq">fatcat:evrjyb2jqjc65kelr3w7wj4phq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220530043611/https://arxiv.org/pdf/2109.07438v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e1/2d/e12d09e91c858f33a810eb69c1bd45a9d9cd73ad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.07438v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, B Aditya Prakash
<span title="2022-04-25">2022</span> <i title="ACM"> Proceedings of the ACM Web Conference 2022 </i> &nbsp;
Most previous works on multi-view time-series forecasting aggregate features from each data view by simple summation or concatenation and do not explicitly model uncertainty for each data view.  ...  Probabilistic time-series forecasting enables reliable decision making across many domains. Most forecasting problems have diverse sources of data containing multiple modalities and structures.  ...  datapoints to model a flexible non-parametric distribution for univariate sequences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3485447.3512037">doi:10.1145/3485447.3512037</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rwafoqgrmbfbbjpzysiwl6kb7q">fatcat:rwafoqgrmbfbbjpzysiwl6kb7q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220428065908/https://dl.acm.org/doi/pdf/10.1145/3485447.3512037" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/4e/fd4eab8f7d4c5f4d0783f336447a12c0af4141c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3485447.3512037"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Metro Passenger Flow Prediction Model Using Attention Based Neural Network

Jun Yang, Xuchen Dong, Shangtai Jin
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Thus, a novel attention mechanism based end-to-end neural network is presented to predict the inbound and outbound passenger flow to improve predictive effect.  ...  INDEX TERMS Attention mechanism, attention based neural network, deep and wide structure, metro passenger flow prediction.  ...  For our neural network, non-category features will be normalized at the same time for training and testing data set.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2973406">doi:10.1109/access.2020.2973406</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ozbr33cv2vbtnirvta77njvgom">fatcat:ozbr33cv2vbtnirvta77njvgom</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108062221/https://ieeexplore.ieee.org/ielx7/6287639/8948470/08995482.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/aa/fc/aafc7adaa8298842dfb00679668cd829f0aa42b8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2973406"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting [article]

Jiawei Zhu, Yujiao Song, Ling Zhao, Haifeng Li
<span title="2020-06-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system.  ...  Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy.  ...  Existing traffic forecasting models can be divided into parametric and non-parametric models.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.11583v1">arXiv:2006.11583v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n5bx23yrhrainc5krpjdptev3i">fatcat:n5bx23yrhrainc5krpjdptev3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710221202/https://arxiv.org/pdf/2006.11583v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.11583v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models [article]

Pramod Vadiraja, Muhammad Ali Chattha
<span title="2020-08-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper focuses on exploring techniques to integrate expert knowledge to the Deep Neural Networks for sequence-to-sequence and time series models to improve their performance and interpretability.  ...  In recent years, with the advent of massive computational power and the availability of huge amounts of data, Deep neural networks have enabled the exploration of uncharted areas in several domains.  ...  [3] presents a technique for Short-Term Load Forecasting(STLF), by the integration of the fuzzy time series (FTS) with convolution neural networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.05972v1">arXiv:2008.05972v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mxl4co34unfvpj7zdvaeb4ktvi">fatcat:mxl4co34unfvpj7zdvaeb4ktvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200818224127/https://arxiv.org/pdf/2008.05972v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.05972v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models [article]

Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
<span title="2020-05-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In many non-forecasting applications where these models have been very successful, the model inputs and outputs are categorical (e.g. words from a fixed vocabulary in natural language processing applications  ...  For forecasting applications, where the time series are typically real-valued, various ad-hoc data transformations have been proposed, but have not been systematically compared.  ...  For example, [37] propose a semi-parametric neural forecasting model that uses the marginal empirical CDFs combined with Gaussian copulas to model non-Gaussian multivariate data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.10111v1">arXiv:2005.10111v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/akiig3mwcfcinci3nnssxcaepe">fatcat:akiig3mwcfcinci3nnssxcaepe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200826084436/https://arxiv.org/pdf/2005.10111v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/a6/85a6daf8a30249a35b3e1d1e2a331b830fb62184.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.10111v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Triple-Stage Attention-Based Multiple Parallel Connection Hybrid Neural Network Model for Conditional Time Series Forecasting

Yepeng Cheng, Yasuhiko Morimoto
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
recurrent neural network (HSAM-RNN) subnetwork for conditional time series prediction with high accuracy.  ...  The experimental results show our TA-SeriesNet is superior to other deep learning models in forecasting accuracy evaluation metrics for high feature dimensional time series datasets.  ...  for time series forecasting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3059861">doi:10.1109/access.2021.3059861</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gnoxyufswnajpjbb5nnapwq4t4">fatcat:gnoxyufswnajpjbb5nnapwq4t4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210228154706/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09355148.pdf?tp=&amp;arnumber=9355148&amp;isnumber=9312710&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/7f/627f3ca6d533a1381a732ab3b409291f1e8b6c80.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3059861"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting

Chenhan Zhang, James J. Q. Yu, Yi Liu
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In this paper, we propose a novel deep learning framework, Spatial-Temporal Graph Attention Networks (ST-GAT).  ...  Attention-based models witnessed extensive developments in recent years and have shown its efficacy in a host of fields, which inspires us to leverage graph-attention-based method to handling traffic network  ...  In the RNN block, we employ a 2-layer LSTM network for extracting time-series feature. The final predictions are generated by a fully-connected neural network in the final output layer. A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2953888">doi:10.1109/access.2019.2953888</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h3mjwe3765bophjanjexw6gs24">fatcat:h3mjwe3765bophjanjexw6gs24</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191207222119/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08903252.pdf?tp=&amp;arnumber=8903252&amp;isnumber=8600701&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/16/fa/16fa3e4ab0aa6cbe51a7d61c669aa184235d3b58.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2953888"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

Jiandong Bai, Jiawei Zhu, Yujiao Song, Ling Zhao, Zhixiang Hou, Ronghua Du, Haifeng Li
<span title="2021-07-15">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw2uac7a5jggbfgqwh2wqy7yxu" style="color: black;">ISPRS International Journal of Geo-Information</a> </i> &nbsp;
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system.  ...  We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively.  ...  Suppose that a time series X i (i = 1, 2, · · · , n), where n is the time series length, is introduced. The design process of soft attention models is introduced as follows.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijgi10070485">doi:10.3390/ijgi10070485</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zobgzetux5erpj4eu3pbob7lta">fatcat:zobgzetux5erpj4eu3pbob7lta</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210722001834/https://res.mdpi.com/d_attachment/ijgi/ijgi-10-00485/article_deploy/ijgi-10-00485-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4f/13/4f13c0e47b7db365be8968ecf93c2a18275a31ee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijgi10070485"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series [article]

Yuanrong Wang, Tomaso Aste
<span title="2022-03-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module.  ...  In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems  ...  In this paper, we design an end-to-end filtered sparse spatialtemporal graph neural network for time-series forecasting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.03991v1">arXiv:2203.03991v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ufl4vpwsozdg5his3nyiacghwu">fatcat:ufl4vpwsozdg5his3nyiacghwu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220310054932/https://arxiv.org/pdf/2203.03991v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/b0/68b095c7186db7f565d085ea178ac53558e44f7f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.03991v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows [article]

Marcel Arpogaus, Marcus Voss, Beate Sick, Mark Nigge-Uricher, Oliver Dürr
<span title="2022-04-29">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Also, they outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.  ...  We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow.  ...  Further, the applied methods can be distinguished between statistical and time series models that often rely on parametric assumptions and non-parametric models from the machine learning domain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.13939v1">arXiv:2204.13939v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jf6s3bnxfvbm7aq36gxwqbv3gq">fatcat:jf6s3bnxfvbm7aq36gxwqbv3gq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220504061705/https://arxiv.org/pdf/2204.13939v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ab/47/ab47f79e1b5b1838a7a5cc892860df76f3301cf3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.13939v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Balanced Graph Structure Learning for Multivariate Time Series Forecasting [article]

Weijun Chen, Yanze Wang, Chengshuo Du, Zhenglong Jia, Feng Liu, Ran Chen
<span title="2022-05-24">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Fully mining the correlation and causation between the variables in a multivariate time series exhibits noticeable results in improving the performance of a time series model.  ...  Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science.  ...  -GTS Graph for time series, which aims to jointly learn a latent graph in the time series and use it for MTS forecasting [16] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.09686v2">arXiv:2201.09686v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yromkt4qlfd4bek7x735wfk55m">fatcat:yromkt4qlfd4bek7x735wfk55m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220526113054/https://arxiv.org/pdf/2201.09686v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cf/80/cf803de5a63ec6893960af15756a8e4766c99e0d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.09686v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Forecasting renewable energy for environmental resilience through computational intelligence

Mansoor Khan, Essam A. Al-Ammar, Muhammad Rashid Naeem, Wonsuk Ko, Hyeong-Jin Choi, Hyun-Koo Kang, Zaher Mundher Yaseen
<span title="2021-08-20">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
In this paper, the data generated from offshore wind turbines are used for power forecasting purposes.  ...  Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy.  ...  To enhance forecasting performance in time series datasets, the optimizer function and capabilities of the LSTM neural network can be further improved.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0256381">doi:10.1371/journal.pone.0256381</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34415924">pmid:34415924</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8378711/">pmcid:PMC8378711</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/paeq4ik5nve6bdqsbuxo7iizxu">fatcat:paeq4ik5nve6bdqsbuxo7iizxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211207003011/https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0256381&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/7d/fb7db0167a640620f90fa54b17871bcb72ce8c1f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0256381"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378711" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Applications of Probabilistic Forecasting in Smart Grids: A Review

Hosna Khajeh, Hannu Laaksonen
<span title="">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment.  ...  Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed.  ...  In order to develop the probabilistic forecasts, the authors applied a multi-attention recurrent neural network (MARNN) to extract the most important contextual information in timeseries forecasting.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app12041823">doi:10.3390/app12041823</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/7983bd9658584546868ebcb5964433cc">doaj:7983bd9658584546868ebcb5964433cc</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ddrmqguhmbe6dpn5nbw3cl43bm">fatcat:ddrmqguhmbe6dpn5nbw3cl43bm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220311230829/https://mdpi-res.com/d_attachment/applsci/applsci-12-01823/article_deploy/applsci-12-01823.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/cd/7ccdd1840c7959bcff304de18977f1c867df4119.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app12041823"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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