Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees

• URL: http://arxiv.org/abs/2003.03657v3
• Date: Sun, 5 Jul 2020 06:15:37 GMT
• Title: Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees
• Authors: Oguzhan Karaahmetoglu (1 and 2) and Suleyman Serdar Kozat (1 and 2) ((1) Bilkent University, (2) DataBoss A.S.)
• Abstract summary: We introduce a novel approach to this problem. Our approach is based on the Hawkes process, which is a non-stationary and self-exciting process. We provide experimental results on real-life data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the spatio-temporal prediction problem, which has attracted the attention of many researchers due to its critical real-life applications. In particular, we introduce a novel approach to this problem. Our approach is based on the Hawkes process, which is a non-stationary and self-exciting point process. We extend the formulations of a standard point process model that can represent time-series data to represent a spatio-temporal data. We model the data as nonstationary in time and space. Furthermore, we partition the spatial region we are working on into subregions via an adaptive decision tree and model the source statistics in each subregion with individual but mutually interacting point processes. We also provide a gradient based joint optimization algorithm for the point process and decision tree parameters. Thus, we introduce a model that can jointly infer the source statistics and an adaptive partitioning of the spatial region. Finally, we provide experimental results on real-life data, which provides significant improvement due to space adaptation and joint optimization compared to standard well-known methods in the literature.

Related papers

• Time Series Analysis by State Space Learning [44.99833362998488]
  Time series analysis by state-space models is widely used in forecasting and extracting unobservable components like level slope, and seasonality, along with explanatory variables. Our research introduces the State Space Learning (SSL), a novel framework and paradigm that leverages the capabilities of statistical learning to construct a comprehensive framework for time series modeling and forecasting.
  arXiv  Detail & Related papers  (2024-08-17T07:04:26Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Reconstructing Spatiotemporal Data with C-VAEs [49.1574468325115]
  Conditional continuous representation of moving regions is commonly used. In this work, we explore the capabilities of Conditional Varitemporal Autoencoder (C-VAE) models to generate realistic representations of regions' evolution.
  arXiv  Detail & Related papers  (2023-07-12T15:34:10Z)
• Mixed moving average field guided learning for spatio-temporal data [0.0]
  We define a novel Bayesian-temporal embedding and a theory-guided machine learning approach to make ensemble forecasts. We use Lipschitz predictors to determine fixed-time and any-time PAC in the batch learning setting. We then test the performance of our learning methodology by using linear predictors and data sets simulated from a dependence- Ornstein-Uhlenbeck process.
  arXiv  Detail & Related papers  (2023-01-02T16:11:05Z)
• Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees [57.67528738886731]
  We study the numerical stability of scalable sparse approximations based on inducing points. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
  arXiv  Detail & Related papers  (2022-10-14T15:20:17Z)
• FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels [82.53569355337586]
  This work offers an efficient solution to temporal point processes inference using general parametric kernels with finite support. The method's effectiveness is evaluated by modeling the occurrence of stimuli-induced patterns from brain signals recorded with magnetoencephalography (MEG) Results show that the proposed approach leads to an improved estimation of pattern latency than the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-10T12:35:02Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning [0.0]
  We introduce here a framework for decomposed-temporal prediction of climate and environmental data using deep learning. Specifically, we introduce functions which can be spatially and mapped on a regular grid allowing the reconstruction of complete-temporal-signal. Applications on simulated real-world data will show the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2020-07-23T07:44:04Z)
• Spatio-temporal Sequence Prediction with Point Processes and Self-organizing Decision Trees [0.0]
  We study the partitioning-temporal prediction problem introduce a point-process-based prediction algorithm. Our algorithm can jointly learn the spatial event and the interaction between these regions through a gradient-based optimization procedure. We compare our approach with state-of-the-art deep learning-based approaches, where we achieve significant performance improvements.
  arXiv  Detail & Related papers  (2020-06-25T14:04:55Z)
• Localized active learning of Gaussian process state space models [63.97366815968177]
  A globally accurate model is not required to achieve good performance in many common control applications. We propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy.
  arXiv  Detail & Related papers  (2020-05-04T05:35:02Z)
• Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels [15.556686221927501]
  Inferring the dynamics of event processes hasly practical applications including crime prediction, and traffic forecasting. We introduce on social-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We replace the spatial kernel calculations by randomized transformations and gradient descent to learn the process.
  arXiv  Detail & Related papers  (2020-03-07T22:21:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.