Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report

• URL: http://arxiv.org/abs/2109.00287v1
• Date: Wed, 1 Sep 2021 09:52:31 GMT
• Title: Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report
• Authors: Elias Alevizos, Alexander Artikis, Georgios Paliouras
• Abstract summary: Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
• Score: 70.7321040534471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting (CEF). Our framework combines two formalisms: a) symbolic automata which are used to encode complex event patterns; and b) prediction suffix trees which can provide a succinct probabilistic description of an automaton's behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.

Related papers

• Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks [17.64833210797824]
  We propose a probabilistic weather forecasting model called Graph-EFM. The model combines a flexible latent-variable formulation with the successful graph-based forecasting framework. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models.
  arXiv  Detail & Related papers  (2024-06-07T09:01:25Z)
• FreDF: Learning to Forecast in Frequency Domain [56.24773675942897]
  Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. We introduce the Frequency-enhanced Direct Forecast (FreDF) which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-04T08:23:41Z)
• A Practical Probabilistic Benchmark for AI Weather Models [0.8278356279004184]
  We show that two leading AI weather models, i.e. GraphCast and Pangu, are tied on the probabilistic CRPS metric. We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive.
  arXiv  Detail & Related papers  (2024-01-27T05:53:16Z)
• Interacting Diffusion Processes for Event Sequence Forecasting [20.380620709345898]
  We introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.
  arXiv  Detail & Related papers  (2023-10-26T22:17:25Z)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Predictive Querying for Autoregressive Neural Sequence Models [23.85426261235507]
  We introduce a general typology for predictive queries in neural autoregressive sequence models. We show that such queries can be systematically represented by sets of elementary building blocks. We leverage this typology to develop new query estimation methods.
  arXiv  Detail & Related papers  (2022-10-12T17:59:36Z)
• Distributional Gradient Boosting Machines [77.34726150561087]
  Our framework is based on XGBoost and LightGBM. We show that our framework achieves state-of-the-art forecast accuracy.
  arXiv  Detail & Related papers  (2022-04-02T06:32:19Z)
• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [51.770998056563094]
  Probabilistic Gradient Boosting Machines (PGBM) is a method to create probabilistic predictions with a single ensemble of decision trees. We empirically demonstrate the advantages of PGBM compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-03T08:32:13Z)
• Generalizing Variational Autoencoders with Hierarchical Empirical Bayes [6.273154057349038]
  We present Hierarchical Empirical Bayes Autoencoder (HEBAE), a computationally stable framework for probabilistic generative models. Our key contributions are two-fold. First, we make gains by placing a hierarchical prior over the encoding distribution, enabling us to adaptively balance the trade-off between minimizing the reconstruction loss function and avoiding over-regularization.
  arXiv  Detail & Related papers  (2020-07-20T18:18:39Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

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.