In this work we propose a novel approach to utilize convolutional neural networks for time series forecasting. The time direction of the sequential data with spatial dimensions $D=1,2$ is considered democratically as the input of a spatiotemporal $(D+1)$-dimensional convolutional neural network. Latter then reduces the data stream from $D +1 \to D$ dimensions followed by an incriminator cell which uses this information to forecast the subsequent time step. We empirically compare this strategy to convolutional LSTM's and LSTM's on their performance on the sequential MNIST and the JSB chorals dataset, respectively. We conclude that temporally folded convolutional neural networks (TFC's) may outperform the conventional recurrent strategies.
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix. On a short time scale, fish-eye cameras on the ground are used to capture cloud displacements causing the local variability of the electricity production. As most of the solar radiation comes directly from the Sun, current forecasting approaches use its position in the image as a reference to interpret the cloud cover dynamics. However, existing Sun tracking methods rely on external data and a calibration of the camera, which requires access to the device. To address these limitations, this s…
Probabilistic Computing Project Massachusetts Institute of Technology（麻省理工学院概率计算项目）
This article proposes a Bayesian nonparametric method for forecasting, imputation, and clustering in sparsely observed, multivariate time series data. The method is appropriate for jointly modeling hundreds of time series with widely varying, non-stationary dynamics. Given a collection of $N$ time series, the Bayesian model first partitions them into independent clusters using a Chinese restaurant process prior. Within a cluster, all time series are modeled jointly using a novel "temporally-reweighted" extension of the Chinese restaurant process mixture. Markov chain Monte Carlo techniques are…
We propose a finite sample based predictor for estimated linear one dimensional time series models and compute the associated total forecasting error. The expression for the error that we present takes into account the estimation error. Unlike existing solutions in the literature, our formulas require neither assumptions on the second order stationarity of the sample nor Monte Carlo simulations for their evaluation. This result is used to prove the pertinence of a new hybrid scheme that we put forward for the forecast of linear temporal aggregates. This novel strategy consists of carrying out …
Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems
ElectroScience Laboratory, The Ohio State University（电科学实验室，俄亥俄州立大学）；Department of Electrical and Computer Engineering, The Ohio State University（电气与计算机工程系，俄亥俄州立大学）；SLAC National Accelerator Laboratory, Stanford University（斯坦福大学SLAC国家加速器实验室）
University of Wollongong（伍伦贡大学）；University of Missouri（密苏里大学）
Integro-difference equation (IDE) models describe the conditional dependence between the spatial process at a future time point and the process at the present time point through an integral operator. Nonlinearity or temporal dependence in the dynamics is often captured by allowing the operator parameters to vary temporally, or by re-fitting a model with a temporally-invariant linear operator in a sliding window. Both procedures tend to be excellent for prediction purposes over small time horizons, but are generally time-consuming and, crucially, do not provide a global prior model for the temp…
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction. To handle this challenging task, we introduce DILATE (DIstortion Loss including shApe and TimE), a new objective function for training deep neural networks. DILATE aims at accurately predicting sudden changes, and explicitly incorporates two terms supporting precise shape and temporal change detection. We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization. We also int…
Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Accurate forecasting of traffic conditions is critical for improving safety, stability, and efficiency of a city transportation system. In reality, it is challenging to produce accurate traffic forecasts due to the complex and dynamic spatiotemporal correlations. Most existing works only consider partial characteristics and features of traffic data, and result in unsatisfactory performances on modeling and forecasting. In this paper, we propose a periodic spatial-temporal deep neural network (PSTN) with three pivotal modules to improve the forecasting performance of traffic conditions through …
Accurate mobile traffic forecast is important for efficient network planning and operations. However, existing traffic forecasting models have high complexity, making the forecasting process slow and costly. In this paper, we analyze some characteristics of mobile traffic such as periodicity, spatial similarity and short term relativity. Based on these characteristics, we propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting. This model employs seasonal differentiation so as to take into account of the temporally repetitive nature of mobile traffic. One of the key featu…
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University of Technology（广东技术大学）；The University of Melbourne（墨尔本大学）
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR China（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle, Newcastle, Australia（新castle大学计算机与信息科学学院）；Hangzhou Innovation Institute of Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
Predicting traffic conditions is tremendously challenging since every road is highly dependent on each other, both spatially and temporally. Recently, to capture this spatial and temporal dependency, specially designed architectures such as graph convolutional networks and temporal convolutional networks have been introduced. While there has been remarkable progress in traffic forecasting, we found that deep-learning-based traffic forecasting models still fail in certain patterns, mainly in event situations (e.g., rapid speed drops). Although it is commonly accepted that these failures are due…
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Criteo AI Lab（Criteo人工智能实验室）
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-driven approach, DINo, that models a PDE's flow with continuous-time dynamics of spatially continuous functions. This is achieved by embedding spatial observations independently of their discretization via Implicit Neural Representations in a small latent space temporally driven by a l…
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedule. Utilizing a state-of-the-art time-series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data with atmospheric forcing, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-squared error (<…
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data
The Hong Kong Polytechnic University（香港理工大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工高等研究院宁波数字孪生研究院）；Shanghai Jiao Tong University（上海交通大学）；Mälardalens University（梅拉达伦大学）
Center for Artificial Intelligence Research and Innovation（人工智能研究与创新中心）
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks. However, these models are usually spatially-transductive -- only fitting the signals for discrete spatial nodes fed in models but unable to generalize to `unseen' spatial points with zero-shot. In comparison, for forecasting tasks on continuous space such as temperature prediction on the earth's surface, the \textit{spatially-inductive} property allows the model to generalize to …
Washington University in St. Louis（圣路易斯华盛顿大学）；NVIDIA（英伟达）
Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develop a methodology to correct, remap, and fine-tune gridded uniform forecasts of FCN so it can be directly compared against observational ground truth, which is sparse and non-uniform in space and time. This is akin to bias correction and post-processing of numerical weather predict…
This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously spill across the entire network. The model is also suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH …
University of Oxford（牛津大学）；University of Reading（雷丁大学）；IUCAA（校际天文学与天体物理学中心）；University of New Hampshire（新罕布什尔大学）；NVIDIA Corporation（英伟达公司）；ASTRA LLC（ASTRA有限责任公司）；Lockheed Martin（洛克希德·马丁公司）；SRI International（SRI国际研究所）
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding models like the Magnetohydrodynamics (MHD) model or sampling spatially and temporally through sparse ground-based stations (SuperMAG). In this paper, we develop a Deep Learning model that forecasts in Spherical Harmonics space 2, replacing reliance on MHD models and providing global coverage at one minute cadence, improving over the current state-of-the-art which relies on feature engineering. We evaluate the performance …
Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting
University of California, Los Angeles（加州大学洛杉矶分校）；Union County Magnet High School（联合县磁石高中）；Huntington Beach High School（亨廷顿海滩高中）；Crean Lutheran High School（克林路德高中）；Portola High School（波托拉高中）；Tsinghua University（清华大学）
Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-demand gap, and then providing efficient incentives to encourage participant movements to maintain the desired balance. In this paper, we propose Deep-Gap, a deep learning approach based on residual learning to predict the gap between mobile crowdsourced service supply and demand at a…
Qatar University（卡塔尔大学）；University of Sydney（悉尼大学）
This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series usi…
University of Southern California（南加州大学）；Snap Inc.（Snap公司）
While mobile social apps have become increasingly important in people's daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users' in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formaliz…
Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Lucas English 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）；Mahdi Abolghasemi School of Mathematics and Physics The University of Queensland St Lucia, QLD 4072, Australia（Mahdi Abolghasemi 数学与物理学院 昆士兰大学 斯塔 Lucia 分校）
IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
Shanghai Academy of AI for Science（上海人工智能科学研究院）；Fudan University（复旦大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Northeastern University（东北大学）
Frontier Development Lab（前沿发展实验室）；Inter University Centre for Astronomy and Astrophysics（校际天文与天体物理中心）；University of Oxford（牛津大学）；University of New Hampshire（新罕布什尔大学）；University of Reading（雷丁大学）；Lockheed Martin Advanced Technology Center（洛克希德·马丁先进技术中心）；NVIDIA Corporation（英伟达公司）；SRI International（斯坦福国际研究院）；ASTRA LLC（阿斯特拉有限责任公司）
Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolution and temporal cadence are of increasing importance to perform prompt necessary mitigation. Since GIC data is proprietary, the time variability of horizontal component of the magnetic field perturbation (dB/dt) is used as a proxy for GICs…
I introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converging algorithm with a fast eigenvector solution. Applications to financial and macro-economic time series show that ForeCA can successfully discover informative structure, which can be used for forecasting as well as classification. The R package ForeCA (http://cran.r-project.org/…
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Goethe Universität Frankfurt（法兰克福大学）；Xidian-FIAS International Joint Research Center（西电-法兰克福高等研究院国际联合研究中心）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理工学院）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）
PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
Faculty of Physics and Astronomy, Ruhr University Bochum（鲁尔大学波鸿分校物理与天文学学院）；Virtual Machining Group, TU Dortmund（多特蒙德工业大学虚拟加工组）；Faculty of Physics（物理学院）
