Spatio-Temporal Time Series Forecasting Using an Iterative Kernel-Based Regression

References

• Andonova S, Elisseeff A, Evgeniou T, Pontil M (2002) A simple algorithm for learning stable machines. Proc. 15th Eur. Conf. Artificial Intelligence (IOS Press, Amsterdam), 513–517.Google Scholar
• André M, Dabo-Niang S, Soubdhan T, Ould-Baba H (2016) Predictive spatio-temporal model for spatially sparse global solar radiation data. Energy 111:599–608.Google Scholar
• Beddar-Wiesing S, D’Inverno GA, Graziani C, Lachi V, Moallemy-Oureh A, Scarselli F, Thomas JM (2024) Weisfeiler–Lehman goes dynamic: An analysis of the expressive power of graph neural networks for attributed and dynamic graphs. Neural Networks 173:106213.Google Scholar
• Cai L, Janowicz K, Mai G, Yan B, Zhu R (2020) Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting. Trans. GIS 24(3):736–755.Google Scholar
• Cawley GC, Talbot NL (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks 17(10):1467–1475.Google Scholar
• Comeau D, Giannakis D, Zhao Z, Majda AJ (2019) Predicting regional and pan-arctic sea ice anomalies with kernel analog forecasting. Climate Dynamics 52:5507–5525.Google Scholar
• Comeau D, Zhao Z, Giannakis D, Majda AJ (2017) Data-driven prediction strategies for low-frequency patterns of north pacific climate variability. Climate Dynamics 48:1855–1872.Google Scholar
• Cornillon P-A, Hengartner N, Jégou N, Matzner-Løber E (2013) Iterative bias reduction: A comparative study. Statist. Comput. 23:777–791.Google Scholar
• Cornillon P-A, Hengartner N, Lefieux V, Matzner-Løber E (2015) Fully nonparametric short term forecasting electricity consumption. Modeling and Stochastic Learning for Forecasting in High Dimensions (Springer, Berlin), 79–93.Google Scholar
• Dambreville R, Blanc P, Chanussot J, Boldo D (2014) Very short term forecasting of the global horizontal irradiance using a spatio-temporal autoregressive model. Renewable Energy 72:291–300.Google Scholar
• Fernández Á, Rabin N, Fishelov D, Dorronsoro JR (2020) Auto-adaptive multi-scale Laplacian pyramids for modeling non-uniform data. Engrg. Appl. Artificial Intelligence 93:103682.Google Scholar
• Fishelov D (1990) A new vortex scheme for viscous flows. J. Comput. Phys. 86(1):211–224.Google Scholar
• Gao Z, Shi X, Wang H, Zhu Y, Wang YB, Li M, Yeung D-Y (2022) Earthformer: Exploring space-time transformers for earth system forecasting. Adv. Neural Inform. Processing Systems 35:25390–25403.Google Scholar
• Guan M, Iyer AP, Kim T (2022) Dynagraph: Dynamic graph neural networks at scale. Proc. 5th ACM SIGMOD Joint Internat. Workshop Graph Data Management Experiences Systems and Network Data Analytics, 1–10.Google Scholar
• Hamdi A, Shaban K, Erradi A, Mohamed A, Rumi SK, Salim FD (2022) Spatiotemporal data mining: A survey on challenges and open problems. Artificial Intelligence Rev. 55:1441–1488.Google Scholar
• Hen B, Fernández A, Rabin N (2022) Improving Laplacian pyramids regression with localization in frequency and time. Proc. Eur. Sympos. Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022) (i6dot.com), 363–368.Google Scholar
• Hengl T, Nussbaum M, Wright MN, Heuvelink GB, Gräler B (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ. 6:e5518.Google Scholar
• Kang L, Joseph VR (2016) Kernel approximation: From regression to interpolation. SIAM/ASA J. Uncertainty Quantification 4(1):112–129.Google Scholar
• Khaldi R, El Afia A, Chiheb R, Tabik S (2023) What is the best RNN-cell structure to forecast each time series behavior? Expert Systems Appl. 215:119140.Google Scholar
• Li M, Cohen I, Mousazadeh S (2014) Multisensory speech enhancement in noisy environments using bone-conducted and air-conducted microphones. Proc. IEEE China Summit Internat. Conf. Signal Inform. Processing (IEEE, New York), 1–5.Google Scholar
• Liang Y, Xia Y, Ke S, Wang Y, Wen Q, Zhang J, Zheng Y, et al. (2023) Airformer: Predicting nationwide air quality in china with transformers. Proc. Conf. AAAI Artificial Intelligence 37:14329–14337.Google Scholar
• Lin T, Wang Y, Liu X, Qiu X (2022) A survey of transformers. AI Open 3:111–132.Google Scholar
• Liu R, Ma Z, Liu Y, Shao Y, Zhao W, Bi J (2020) Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach. Environment. Internat. 142:105823.Google Scholar
• Mohamed A, Qian K, Elhoseiny M, Claudel C (2020) Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. Proc. IEEE/CVF Conf. Computer Vision Pattern Recognition (IEEE, Piscataway, NJ), 14424–14432.Google Scholar
• Müller M (2007) Dynamic time warping. Information Retrieval for Music and Motion (Springer, Berlin, Heidelberg), 69–84.Google Scholar
• Nadaraya EA (1964) On estimating regression. Theory Probability Appl. 9(1):141–142.Google Scholar
• Rabin N (2020) Multi-directional Laplacian pyramids for completion of missing data entries. Proc. Eur. Sympos. Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020) (i6dot.com), 709–714.Google Scholar
• Rabin N, Coifman RR (2012) Heterogeneous data sets representation and learning using diffusion maps and Laplacian pyramids. Proc. SIAM Internat. Conf. Data Mining (SIAM, Philadelphia), 189–199.Google Scholar
• Rabin N, Fishelov D (2019) Two directional Laplacian pyramids with application to data imputation. Adv. Comput. Math. 45(4):2123–2146.Google Scholar
• Rabin N, Fernández Á, Fishelov D (2023) Multiscale extensions for enhancing coarse grid computations. J. Comput. Appl. Math. 427:115116.Google Scholar
• Rao KV, Govardhan A, Rao KC (2012) Spatiotemporal data mining: Issues, tasks and applications. Internat. J. Computer Sci. Engrg. Survey 3(1):39.Google Scholar
• Rozemberczki B, Scherer P, He Y, Panagopoulos G, Riedel A, Astefanoaei M, Kiss O, et al. (2021) Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models. Proc. 30th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 4564–4573.Google Scholar
• Shekhar S, Jiang Z, Ali RY, Eftelioglu E, Tang X, Gunturi VM, Zhou X (2015) Spatiotemporal data mining: A computational perspective. ISPRS Internat. J. Geoinform. 4(4):2306–2338.Google Scholar
• Shih S-Y, Sun F-K, Lee H-y (2019) Temporal pattern attention for multivariate time series forecasting. Machine Learn. 108:1421–1441.Google Scholar
• Souza A, Mesquita D, Kaski S, Garg V (2022) Provably expressive temporal graph networks. Adv. Neural Inform. Processing Systems 35:32257–32269.Google Scholar
• Wang S, Cao J, Philip SY (2020) Deep learning for spatio-temporal data mining: A survey. IEEE Trans. Knowledge Data Engrg. 34(8):3681–3700.Google Scholar
• Wang J, Huyen D, Woznica A, Kalousis A (2011) Metric learning with multiple kernels. Shawe-Taylor J, Zemel R, Bartlett P, Pereira F, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems, vol. 24 (Curran Associates, Inc., Red Hook, NY).Google Scholar
• Wen Q, Zhou T, Zhang C, Chen W, Ma Z, Yan J, Sun L (2023) Transformers in time series: A survey. IJCAI ’23: Proc. Thirty-Second Internat. Joint Conf. Artificial Intelligence (Macao, S.A.R), 6778–6786.Google Scholar
• Wood SN (2017) Generalized Additive Models: An Introduction with R (CRC Press, Boca Raton, FL).Google Scholar
• Xia Z, Stewart K (2023) A counterfactual analysis of opioid-involved deaths during the COVID-19 pandemic using a spatiotemporal random forest modeling approach. Health Place 80:102986.Google Scholar
• Xu B, Lu X, Bai Y, Xu D, Cui X (2023) A multi-kernel-based spatiotemporal modeling approach for energy transfer of complex thermal processes and its applications. Internat. J. Heat Mass Transfer 216:124597.Google Scholar
• Xu M, Dai W, Liu C, Gao X, Lin W, Qi G-J, Xiong H (2020) Spatial-temporal transformer networks for traffic flow forecasting. Preprint, submitted January 9, https://arxiv.org/abs/2001.02908.Google Scholar
• Yang D, Ye Z, Lim LHI, Dong Z (2015) Very short term irradiance forecasting using the lasso. Solar Energy 114:314–326.Google Scholar
• Yang D, Dong Z, Reindl T, Jirutitijaroen P, Walsh WM (2014) Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage. Solar Energy 103:550–562.Google Scholar
• Zeng A, Chen M, Zhang L, Xu Q (2023) Are transformers effective for time series forecasting? Proc. Conf. AAAI Artificial Intelligence, vol. 37 (AAAI Press, Palo Alto, CA), 11121–11128.Google Scholar
• Zhang X, Zhong C, Zhang J, Wang T, Ng WW (2023) Robust recurrent neural networks for time series forecasting. Neurocomputing 526:143–157.Google Scholar