Journal articles: 'Spatiotemporal forecasting'

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Li, Cheng, Weimin Zheng, and Peng Ge. "Tourism demand forecasting with spatiotemporal features." Annals of Tourism Research 94 (May 2022): 103384. http://dx.doi.org/10.1016/j.annals.2022.103384.

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Lin, Xu, Hongyue Wang, Qingqing Zhang, Chaolong Yao, Changxin Chen, Lin Cheng, and Zhaoxiong Li. "A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting." Remote Sensing 14, no. 7 (April 2, 2022): 1717. http://dx.doi.org/10.3390/rs14071717.

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In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.

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Oliveira, Mariana, Luís Torgo, and Vítor Santos Costa. "Evaluation Procedures for Forecasting with Spatiotemporal Data." Mathematics 9, no. 6 (March 23, 2021): 691. http://dx.doi.org/10.3390/math9060691.

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The increasing use of sensor networks has led to an ever larger number of available spatiotemporal datasets. Forecasting applications using this type of data are frequently motivated by important domains such as environmental monitoring. Being able to properly assess the performance of different forecasting approaches is fundamental to achieve progress. However, traditional performance estimation procedures, such as cross-validation, face challenges due to the implicit dependence between observations in spatiotemporal datasets. In this paper, we empirically compare several variants of cross-validation (CV) and out-of-sample (OOS) performance estimation procedures, using both artificially generated and real-world spatiotemporal datasets. Our results show both CV and OOS reporting useful estimates, but they suggest that blocking data in space and/or in time may be useful in mitigating CV’s bias to underestimate error. Overall, our study shows the importance of considering data dependencies when estimating the performance of spatiotemporal forecasting models.

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Pavlyuk, Dmitry. "Temporal Aggregation Effects in Spatiotemporal Traffic Modelling." Sensors 20, no. 23 (December 4, 2020): 6931. http://dx.doi.org/10.3390/s20236931.

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Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.

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Muñoz-Organero, Mario, and Paula Queipo-Álvarez. "Deep Spatiotemporal Model for COVID-19 Forecasting." Sensors 22, no. 9 (May 5, 2022): 3519. http://dx.doi.org/10.3390/s22093519.

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COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies.

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., V. Nourani, A. A. Moghaddam ., A. O. Nadiri ., and V. P. Singh . "Forecasting Spatiotemporal Water Levels of Tabriz Aquifer." Trends in Applied Sciences Research 3, no. 4 (April 1, 2008): 319–29. http://dx.doi.org/10.3923/tasr.2008.319.329.

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López, Cristóbal, Alberto Álvarez, and Emilio Hernández-García. "Forecasting Confined Spatiotemporal Chaos with Genetic Algorithms." Physical Review Letters 85, no. 11 (September 11, 2000): 2300–2303. http://dx.doi.org/10.1103/physrevlett.85.2300.

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Ermagun, Alireza, and David Levinson. "Spatiotemporal traffic forecasting: review and proposed directions." Transport Reviews 38, no. 6 (March 6, 2018): 786–814. http://dx.doi.org/10.1080/01441647.2018.1442887.

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Pavlyuk. "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting." Algorithms 13, no. 2 (February 13, 2020): 39. http://dx.doi.org/10.3390/a13020039.

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Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: video prediction and spatiotemporal traffic forecasting. The similarities of the video stream and citywide traffic data structures are discovered and analogues between historical development and modern states of the methodologies are presented and discussed. The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set. The list of transferred techniques includes spatial filtering by predefined kernels in combination with time series models and spectral graph convolutional artificial neural networks. The obtained models’ forecasting performance is compared to the baseline traffic forecasting models: non-spatial time series models and spatially regularized vector autoregression models. We conclude that the application of video prediction models and algorithms for urban traffic forecasting is effective both in terms of observed forecasting accuracy and development, and training efforts. Finally, we discuss problems and obstacles of transferring methodologies and present potential directions for further research.

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Xiong, Liyan, Weihua Ding, Xiaohui Huang, and Weichun Huang. "CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting." Mathematical Problems in Engineering 2022 (July 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.

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Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.

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Prestemon, Jeffrey P., María L. Chas-Amil, Julia M. Touza, and Scott L. Goodrick. "Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations." International Journal of Wildland Fire 21, no. 6 (2012): 743. http://dx.doi.org/10.1071/wf11049.

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We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P – PAR(P) models – significantly out-perform competing alternative models over both in-sample and out-of-sample datasets, reducing out-of-sample root-mean-squared errors by an average of 15%. PAR(P) and static Poisson models included covariates deriving from crime theory, including the temporal and spatiotemporal autoregressive time series components. Estimates indicate highly significant autoregressive components, lasting up to 3 days, and spatiotemporal autoregression, lasting up to 2 days. Models also applied to predict the effect of increased arrest rates for illegal intentional firesetting indicate that the direct long-run effect of an additional firesetting arrest, summed across forest districts in Galicia, is –139.6 intentional wildfires, equivalent to a long-run elasticity of –0.94.

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Pavlyuk, Dmitry. "Spatiotemporal cross-validation of urban traffic forecasting models." Transportation Research Procedia 52 (2021): 179–86. http://dx.doi.org/10.1016/j.trpro.2021.01.020.

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Kaboudan, M. A. "SPATIOTEMPORAL FORECASTING OF HOME PRICES: A GIS APPLICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 95–99. http://dx.doi.org/10.3182/20050703-6-cz-1902.02251.

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Director, Hannah M., Adrian E. Raftery, and Cecilia M. Bitz. "Improved Sea Ice Forecasting through Spatiotemporal Bias Correction." Journal of Climate 30, no. 23 (December 2017): 9493–510. http://dx.doi.org/10.1175/jcli-d-17-0185.1.

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A new method, called contour shifting, is proposed for correcting the bias in forecasts of contours such as sea ice concentration above certain thresholds. Retrospective comparisons of observations and dynamical model forecasts are used to build a statistical spatiotemporal model of how predicted contours typically differ from observed contours. Forecasted contours from a dynamical model are then adjusted to correct for expected errors in their location. The statistical model changes over time to reflect the changing error patterns that result from reducing sea ice cover in the satellite era in both models and observations. For an evaluation period from 2001 to 2013, these bias-corrected forecasts are on average more accurate than the unadjusted dynamical model forecasts for all forecast months in the year at four different lead times. The total area, which is incorrectly categorized as containing sea ice or not, is reduced by 3.3 × 105 km2 (or 21.3%) on average. The root-mean-square error of forecasts of total sea ice area is also reduced for all lead times.

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Chai, Songjian, Zhao Xu, Youwei Jia, and Wai Kin Wong. "A Robust Spatiotemporal Forecasting Framework for Photovoltaic Generation." IEEE Transactions on Smart Grid 11, no. 6 (November 2020): 5370–82. http://dx.doi.org/10.1109/tsg.2020.3006085.

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Lenzi, Amanda, and Marc G. Genton. "Spatiotemporal probabilistic wind vector forecasting over Saudi Arabia." Annals of Applied Statistics 14, no. 3 (September 2020): 1359–78. http://dx.doi.org/10.1214/20-aoas1347.

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Jiao, Xiaoying, Gang Li, and Jason Li Chen. "Forecasting international tourism demand: a local spatiotemporal model." Annals of Tourism Research 83 (July 2020): 102937. http://dx.doi.org/10.1016/j.annals.2020.102937.

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Abirami, S., and P. Chitra. "Regional air quality forecasting using spatiotemporal deep learning." Journal of Cleaner Production 283 (February 2021): 125341. http://dx.doi.org/10.1016/j.jclepro.2020.125341.

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You, Yujie, Le Zhang, Peng Tao, Suran Liu, and Luonan Chen. "Spatiotemporal Transformer Neural Network for Time-Series Forecasting." Entropy 24, no. 11 (November 14, 2022): 1651. http://dx.doi.org/10.3390/e24111651.

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Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

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Wang, Yi, and Changfeng Jing. "Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting." ISPRS International Journal of Geo-Information 11, no. 2 (February 1, 2022): 102. http://dx.doi.org/10.3390/ijgi11020102.

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Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people’s daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spatiotemporal graph neural network has attracted attention from academic and business domains for its powerful spatiotemporal pattern capturing capability. However, the existing work focused on the overall traffic network instead of traffic nodes, and the latter can be useful in learning different patterns among nodes. Moreover, there are few works that captured fine-grained node-specific spatiotemporal feature extraction at multiple scales at the same time. To unfold the node pattern, a node embedding parameter was designed to adaptively learn nodes patterns in adjacency matrix and graph convolution layer. To address this multi-scale problem, we adopted the idea of Res2Net and designed a hierarchical temporal attention layer and hierarchical adaptive graph convolution layer. Based on the above methods, a novel model, called Temporal Residual II Graph Convolutional Network (Tres2GCN), was proposed to capture not only multi-scale spatiotemporal but also fine-grained features. Tres2GCN was validated by comparing it with 10 baseline methods using two public traffic volume datasets. The results show that our model performs good accuracy, outperforming existing methods by up to 9.4%.

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Oh, Myeongchan, Chang Ki Kim, Boyoung Kim, Changyeol Yun, Yong-Heack Kang, and Hyun-Goo Kim. "Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery." Energies 14, no. 8 (April 15, 2021): 2216. http://dx.doi.org/10.3390/en14082216.

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Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

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He, Zichao, Chunna Zhao, and Yaqun Huang. "Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network." Applied Sciences 12, no. 11 (June 5, 2022): 5731. http://dx.doi.org/10.3390/app12115731.

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Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments.

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Karimi, Ahmad Maroof, Yinghui Wu, Mehmet Koyuturk, and Roger H. French. "Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15323–30. http://dx.doi.org/10.1609/aaai.v35i17.17799.

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In recent years, a large number of photovoltaic (PV) systems have been added to the electrical grid as well as installed as off-grid systems. The trend suggests that the deployment of PV systems will continue to rise in the future. Thus, accurate forecasting of PV performance is critical for the reliability of PV systems. Due to the complex non-linear variability in power output of the PV systems, forecasting PV power is a non-trivial task. This variability affects the stability and planning of a power system network, and accurate forecasting of the performance of the PV system can reduce the uncertainty caused during PV operation. In this work, we leverage spatial and temporal coherence among the power plants for PV power forecasting. Our approach is motivated by the observation that power plants in a region undergo similar environmental exposure. Thus, one power plant’s performance can help improve the forecast of other power plants' power values in the region. We utilize the relationship between PV plants to build a spatiotemporal graph neural network (st-GNN) and train machine learning models to forecast the PV power. The computational experiments on large-scale data from a network of 316 systems show that spatiotemporal forecasting of PV power performs significantly better than a model that only applies temporal convolution to isolated systems or nodes. Furthermore, the longer the future forecast time, the difference between the spatiotemporal forecasting and the isolated system forecast when only temporal convolution is applied increases further.

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Geng, Liangchao, Huantong Geng, Jinzhong Min, Xiaoran Zhuang, and Yu Zheng. "AF-SRNet: Quantitative Precipitation Forecasting Model Based on Attention Fusion Mechanism and Residual Spatiotemporal Feature Extraction." Remote Sensing 14, no. 20 (October 12, 2022): 5106. http://dx.doi.org/10.3390/rs14205106.

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Reliable quantitative precipitation forecasting is essential to society. At present, quantitative precipitation forecasting based on weather radar represents an urgently needed, yet rather challenging. However, because the Z-R relation between radar and rainfall has several parameters in different areas, and because rainfall varies with seasons, traditional methods cannot capture high-resolution spatiotemporal features. Therefore, we propose an attention fusion spatiotemporal residual network (AF-SRNet) to forecast rainfall precisely for the weak continuity of convective precipitation. Specifically, the spatiotemporal residual network is designed to extract the deep spatiotemporal features of radar echo and precipitation data. Then, we combine the radar echo feature and precipitation feature as the input of the decoder through the attention fusion block; after that, the decoder forecasts the rainfall for the next two hours. We train and evaluate our approaches on the historical data from the Jiangsu Meteorological Observatory. The experimental results show that AF-SRNet can effectively utilize multiple inputs and provides more precise nowcasting of convective precipitation.

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Geng, Xu, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, and Yan Liu. "Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3656–63. http://dx.doi.org/10.1609/aaai.v33i01.33013656.

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Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.

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Halim, Calvin Janitra, and Kazuhiko Kawamoto. "2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting." Sensors 20, no. 15 (July 28, 2020): 4195. http://dx.doi.org/10.3390/s20154195.

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Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model. To solve this, we propose a model that retains the spatial aspect of the target sequence with a DMM that consists of 2D convolutional neural networks. We then show the robustness of our method to data with large variance compared with naive forecast, vanilla DMM, and convolutional long short-term memory (LSTM) using synthetic data, even outperforming the DNN models over a longer forecast period. We also point out the limitations of our model when forecasting real-world precipitation data and the possible future work that can be done to address these limitations, along with additional future research potential.

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Du, Liufeng, Linghua Zhang, and Xu Wang. "Spatiotemporal Feature Learning Based Hour-Ahead Load Forecasting for Energy Internet." Electronics 9, no. 1 (January 20, 2020): 196. http://dx.doi.org/10.3390/electronics9010196.

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In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead load forecasting based on the deep learning paradigm. The proposed scheme involves three aspects. First, we formulate a historical load matrix (HLM) with spatiotemporal correlation combined with the EI scenario and then create a three-dimensional historical load tensor (HLT) that contains the HLMs for multiple consecutive time points before the forecasted hour. Second, we preprocess the HLT leveraging a novel low rank decomposition algorithm and different load gradients, aiming to provide a forecasting model with richer input data. Third, we develop a deep forecasting framework (called the 3D CNN-GRU) featuring a feature learning module followed by a regression module, in which the 3D convolutional neural network (3D CNN) is used to extract the desired feature sequences with time attributes, while the gated recurrent unit (GRU) is responsible for mapping the sequences to the forecast values. By feeding the corresponding load label into the 3D CNN-GRU, our proposed scheme can carry out forecasting tasks for any zone covered by the HLM. The results of self-evaluation and a comparison with several state-of-the-art methods demonstrate the superiority of the proposed scheme.

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Zhang, Chaoyun, Marco Fiore, Iain Murray, and Paul Patras. "CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10851–58. http://dx.doi.org/10.1609/aaai.v35i12.17296.

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This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (DConv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input. This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning. The DConv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms. We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e. mobile service traffic forecasting and air quality indicator forecasting. Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of competitor neural network models.

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Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling." Future Internet 13, no. 9 (August 30, 2021): 225. http://dx.doi.org/10.3390/fi13090225.

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Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

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Jiao, Xiaoying, Jason Li Chen, and Gang Li. "Forecasting tourism demand: Developing a general nesting spatiotemporal model." Annals of Tourism Research 90 (September 2021): 103277. http://dx.doi.org/10.1016/j.annals.2021.103277.

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Bulanadi, Jehan, Gilbert Tumibay, and Mary Ann Quioc. "Spatiotemporal Data Analysis and Forecasting Model for Forestland Rehabilitation." International Journal of Computing Sciences Research 3, no. 4 (December 1, 2019): 229–45. http://dx.doi.org/10.25147/ijcsr.2017.001.1.36.

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Zhou, Fan, Qing Yang, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ashfaq Khokhar. "Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting." IEEE Internet of Things Journal 7, no. 7 (July 2020): 6414–28. http://dx.doi.org/10.1109/jiot.2020.2974494.

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Yue, Yang, and Anthony Gar-On Yeh. "Spatiotemporal traffic-flow dependency and short-term traffic forecasting." Environment and Planning B: Planning and Design 35, no. 5 (2008): 762–71. http://dx.doi.org/10.1068/b33090.

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Nourani, Vahid, Asghar Asghari Mogaddam, and Ata Ollah Nadiri. "An ANN-based model for spatiotemporal groundwater level forecasting." Hydrological Processes 22, no. 26 (December 30, 2008): 5054–66. http://dx.doi.org/10.1002/hyp.7129.

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McDermott, Patrick L., Christopher K. Wikle, and Joshua Millspaugh. "A hierarchical spatiotemporal analog forecasting model for count data." Ecology and Evolution 8, no. 1 (December 7, 2017): 790–800. http://dx.doi.org/10.1002/ece3.3621.

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Acquah, Moses Amoasi, Yuwei Jin, Byeong-Chan Oh, Yeong-Geon Son, and Sung-Yul Kim. "Spatiotemporal Sequence-to-Sequence Clustering for Electric Load Forecasting." IEEE Access 11 (2023): 5850–63. http://dx.doi.org/10.1109/access.2023.3235724.

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Yu, Fanhua, Huibowen Hao, and Qingliang Li. "An Ensemble 3D Convolutional Neural Network for Spatiotemporal Soil Temperature Forecasting." Sustainability 13, no. 16 (August 16, 2021): 9174. http://dx.doi.org/10.3390/su13169174.

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Soil temperature (ST) plays an important role in agriculture and other fields, and has a close relationship with plant growth and development. Therefore, accurate ST prediction methods are widely needed. Deep learning (DL) models have been widely applied for ST prediction. However, the traditional DL models may fail to capture the spatiotemporal relationship due to its complex dependency under different related hydrologic variables. Hence, the DL models with Ensemble Empirical Mode Decomposition (EEMD) are proposed in this study. The proposed models can capture more complex spatiotemporal relationship after decomposing the ST into different intrinsic mode functions. Therefore, the performance of models is further improved. The results show that the performance of DL models with EEMD are better than that of corresponding DL models without EEMD. Moreover, EEMD-Conv3d has the best performance among all the experimental models. It has the highest R2 ranging from 0.9826 to 0.9893, the lowest RMSE ranging from 1.3096 to 1.6497 and the lowest MAE ranging from 0.9656 to 1.2056 in predicting ST at the lead time from one to five days. In addition, the lines between predicted ST and observed ST are closer to the ideal line (y = x) than other DL models. The results show that our EEMD-Conv3D can better capture spatiotemporal correlation and is an applicable method for predicting spatiotemporal ST.

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Chen, Suting, Song Zhang, Huantong Geng, Yaodeng Chen, Chuang Zhang, and Jinzhong Min. "Strong Spatiotemporal Radar Echo Nowcasting Combining 3DCNN and Bi-Directional Convolutional LSTM." Atmosphere 11, no. 6 (May 29, 2020): 569. http://dx.doi.org/10.3390/atmos11060569.

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In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convolutional long short-term memory. The model first constructs dimensions of input data and gets 3D tensor data with spatiotemporal features, extracts local short-term spatiotemporal features of radar echoes through 3D convolution networks, then utilizes constructed bi-directional convolutional LSTM to learn global long-term spatiotemporal feature dependencies, and finally realizes the forecast of echo image changes by forecasting network. This structure can capture the spatiotemporal correlation of radar echoes in continuous motion fully and realize more accurate forecast of moving trend of short-term radar echoes within a region. The samples of radar echo images recorded by Shenzhen and Hong Kong meteorological stations are used for experiments, the results show that the critical success index (CSI) of this proposed model for eight predicted echoes reaches 0.578 when the echo threshold is 10 dBZ, the false alarm ratio (FAR) is 20% lower than convolutional LSTM network (ConvLSTM), and the mean square error (MSE) is 16% lower than the real-time optical flow by variational method (ROVER), which outperforms the current state-of-the-art radar echo nowcasting methods.

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Cao, Yang, Detian Liu, Qizheng Yin, Fei Xue, and Hengliang Tang. "MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting." Journal of Advanced Transportation 2022 (June 17, 2022): 1–15. http://dx.doi.org/10.1155/2022/2811961.

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Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.

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Zhou, Qianqian, Nan Chen, and Siwei Lin. "FASTNN: A Deep Learning Approach for Traffic Flow Prediction Considering Spatiotemporal Features." Sensors 22, no. 18 (September 13, 2022): 6921. http://dx.doi.org/10.3390/s22186921.

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Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic flow forecasting can provide an effective reference for implementing traffic management strategies, developing travel route planning, and public transportation risk assessment. Recent deep learning approaches of spatiotemporal neural networks to predict traffic flow show promise, but could be difficult to separately model the spatiotemporal aggregation in traffic data and intrinsic correlation or redundancy of spatiotemporal features extracted by the filter of the convolutional network. This can introduce biases in the predictions that interfere with subsequent planning decisions in transportation. To solve the mentioned problem, the filter attention-based spatiotemporal neural network (FASTNN) was proposed in this paper. First, the model used 3-dimensional convolutional neural networks to extract universal spatiotemporal dependencies from three types of historical traffic flow, the residual units were employed to prevent network degradation. Then, the filter spatial attention module was constructed to quantify the spatiotemporal aggregation of the features, thus enabling dynamic adjustment of the spatial weights. To model the intrinsic correlation and redundancy of features, this paper also constructed a lightweight module, named matrix factorization based resample module, which automatically learned the intrinsic correlation of the same features to enhance the concentration of the model on information-rich features, and used matrix factorization to reduce the redundant information between different features. The FASTNN has experimented on two large-scale real datasets (TaxiBJ and BikeNYC), and the experimental results show that the FASTNN has better prediction performance than various baselines and variant models.

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Lee, Kyungeun, and Wonjong Rhee. "DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting." Transportation Research Part C: Emerging Technologies 134 (January 2022): 103466. http://dx.doi.org/10.1016/j.trc.2021.103466.

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Zhao, Liang, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting." IEEE Transactions on Knowledge and Data Engineering 29, no. 5 (May 1, 2017): 1059–72. http://dx.doi.org/10.1109/tkde.2017.2657624.

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Castro, Rafaela, Yania M. Souto, Eduardo Ogasawara, Fabio Porto, and Eduardo Bezerra. "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting." Neurocomputing 426 (February 2021): 285–98. http://dx.doi.org/10.1016/j.neucom.2020.09.060.

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Gilanifar, Mostafa, Hui Wang, Lalitha Madhavi Konila Sriram, Eren Erman Ozguven, and Reza Arghandeh. "Multitask Bayesian Spatiotemporal Gaussian Processes for Short-Term Load Forecasting." IEEE Transactions on Industrial Electronics 67, no. 6 (June 2020): 5132–43. http://dx.doi.org/10.1109/tie.2019.2928275.

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Wu, Yuankai, Dingyi Zhuang, Aurelie Labbe, and Lijun Sun. "Inductive Graph Neural Networks for Spatiotemporal Kriging." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4478–85. http://dx.doi.org/10.1609/aaai.v35i5.16575.

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Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, while little attention has been paid to the kriging problem---recovering signals for unsampled locations/sensors. Most existing scalable kriging methods (e.g., matrix/tensor completion) are transductive, and thus full retraining is required when we have a new sensor to interpolate. In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data for unsampled sensors on a network/graph structure. To generalize the effect of distance and reachability, we generate random subgraphs as samples and the corresponding adjacency matrix for each sample. By reconstructing all signals on each sample subgraph, IGNNK can effectively learn the spatial message passing mechanism. Empirical results on several real-world spatiotemporal datasets demonstrate the effectiveness of our model. In addition, we also find that the learned model can be successfully transferred to the same type of kriging tasks on an unseen dataset. Our results show that: 1) GNN is an efficient and effective tool for spatial kriging; 2) inductive GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be transferred to new graph structures and 4) IGNNK can be used to generate virtual sensors.

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Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.

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As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.

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Jia, Hongwei, Haiyong Luo, Hao Wang, Fang Zhao, Qixue Ke, Mingyao Wu, and Yunyun Zhao. "ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning." Sensors 20, no. 16 (August 14, 2020): 4574. http://dx.doi.org/10.3390/s20164574.

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Passenger flow prediction has drawn increasing attention in the deep learning research field due to its great importance in traffic management and public safety. The major challenge of this essential task lies in multiple spatiotemporal correlations that exhibit complex non-linear correlations. Although both the spatial and temporal perspectives have been considered in modeling, most existing works have ignored complex temporal correlations or underlying spatial similarity. In this paper, we identify the unique spatiotemporal correlation of urban metro flow, and propose an attention-based deep spatiotemporal network with multi-task learning (ADST-Net) at a citywide level to predict the future flow from historical observations. ADST-Net uses three independent channels with the same structure to model the recent, daily-periodic and weekly-periodic complicated spatiotemporal correlations, respectively. Specifically, each channel uses the framework of residual networks, the rectified block and the multi-scale convolutions to mine spatiotemporal correlations. The residual networks can effectively overcome the gradient vanishing problem. The rectified block adopts an attentional mechanism to automatically reweigh measurements at different time intervals, and the multi-scale convolutions are used to extract explicit spatial relationships. ADST-Net also introduces an external embedding mechanism to extract the influence of external factors on flow prediction, such as weather conditions. Furthermore, we enforce multi-task learning to utilize transition passenger flow volume prediction as an auxiliary task during the training process for generalization. Through this model, we can not only capture the steady trend, but also the sudden changes of passenger flow. Extensive experimental results on two real-world traffic flow datasets demonstrate the obvious improvement and superior performance of our proposed algorithm compared with state-of-the-art baselines.

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Han, Xu, and Shicai Gong. "LST-GCN: Long Short-Term Memory Embedded Graph Convolution Network for Traffic Flow Forecasting." Electronics 11, no. 14 (July 17, 2022): 2230. http://dx.doi.org/10.3390/electronics11142230.

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Traffic flow prediction is an important part of the intelligent transportation system. Accurate traffic flow prediction is of great significance for strengthening urban management and facilitating people’s travel. In this paper, we propose a model named LST-GCN to improve the accuracy of current traffic flow predictions. We simulate the spatiotemporal correlations present in traffic flow prediction by optimizing GCN (graph convolutional network) parameters using an LSTM (long short-term memory) network. Specifically, we capture spatial correlations by learning topology through GCN networks and temporal correlations by embedding LSTM networks into the training process of GCN networks. This method improves the traditional method of combining the recurrent neural network and graph neural network in the original spatiotemporal traffic flow prediction, so it can better capture the spatiotemporal features existing in the traffic flow. Extensive experiments conducted on the PEMS dataset illustrate the effectiveness and outperformance of our method compared with other state-of-the-art methods.

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Das, Someshwar, S. V. Singh, E. N. Rajagopal, and Robert Gall. "Mesoscale Modeling for Mountain Weather Forecasting Over the Himalayas." Bulletin of the American Meteorological Society 84, no. 9 (September 1, 2003): 1237–44. http://dx.doi.org/10.1175/bams-84-9-1237.

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Severe weather has a more calamitous effect in the mountainous region because the terrain is complex and the economy is poorly developed and fragile. Such weather systems occurring on a small spatiotemporal scale invite application of models with fine-grid resolution and observations from radars and satellites besides the conventional observations for forecasting and disaster mitigation.

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TSONIS, A. A. "THE IMPACT OF NONLINEAR DYNAMICS IN THE ATMOSPHERIC SCIENCES." International Journal of Bifurcation and Chaos 11, no. 04 (April 2001): 881–902. http://dx.doi.org/10.1142/s0218127401002663.

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In this review some of the achievements in atmospheric sciences that resulted from chaos theory and its implications are discussed. They include El Niño dynamics, physics and spatiotemporal dynamics of the general circulation, and ensemble forecasting.

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Journal articles on the topic 'Spatiotemporal forecasting'

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