Warped Input Gaussian Processes for Time Series Forecasting

Igor Vinokur, David Tolpin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Time series forecasting plays a vital role in system monitoring and novelty detection. However, commonly used forecasting methods are not suited for handling non-stationarity, while existing methods for forecasting in non-stationary time series are often complex to implement and involve expensive computations. We introduce a Gaussian process-based model for handling of non-stationarity. The warping is achieved non-parametrically, through imposing a prior on the relative change of distance between subsequent observation inputs. The model allows the use of general gradient optimization algorithms for training and incurs only a small computational overhead on training and prediction. The model finds its applications in forecasting in non-stationary time series with either gradually varying volatility, presence of change points, or a combination thereof. We implement the model in a probabilistic programming framework, evaluate on synthetic and real-world time series data comparing against both broadly used baselines and known state-of-the-art approaches and show that the model exhibits state-of-the-art forecasting performance at a lower implementation and computation cost, enabling efficient applications in diverse fields of system monitoring and novelty detection.

Original languageEnglish
Title of host publicationCyber Security Cryptography and Machine Learning - 5th International Symposium, CSCML 2021, Proceedings
EditorsShlomi Dolev, Oded Margalit, Benny Pinkas, Alexander Schwarzmann
PublisherSpringer Science and Business Media Deutschland GmbH
Pages205-220
Number of pages16
ISBN (Print)9783030780852
DOIs
StatePublished - 1 Jan 2021
Event5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021 - Be'er Sheva, Israel
Duration: 8 Jul 20219 Jul 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12716 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2021
Country/TerritoryIsrael
CityBe'er Sheva
Period8/07/219/07/21

Keywords

  • Gaussian processes
  • Non-stationarity
  • Probabilistic programming
  • Time series

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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