Learning Causally-Generated Stationary Time Series
We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena. The CGPCM is a generative model in which white noise is passed through a causal, nonparametric-window moving-average filter, a construction that we show to be equivalent to a Gaussian process with a nonparametric kernel that is biased towards causally-generated signals. We develop enhanced variational inference and learning schemes for the CGPCM and its previous acausal variant, the GPCM (Tobar et al., 2015b), that significantly improve statistical accuracy. These modelling and inferential contributions are demonstrated on a range of synthetic and real-world signals.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series AnalysisVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Application of time-series quantum generative model to financial data
Despite proposing a quantum generative model for time series that successfully learns correlated series with multiple Brownian motions, the model has not been adapted and evaluated for financial problems. In this study, …
Missing ValuesTime SeriesTime Series GenerationNon-stationary dynamic Bayesian networks
A principled mechanism for identifying conditional dependencies in time-series data is provided through structure learning of dynamic Bayesian networks (DBNs). An important assumption of DBN structure learning is that th…
Time SeriesTime Series AnalysisMultiple Change Point Estimation in Stationary Ergodic Time Series
Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed. The data are assumed to have been generated by …
Time SeriesTime Series AnalysisA Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks
The stochastic kinetics of BRN are described by a chemical master equation (CME) and the underlying laws of mass action. The CME must be usually solved numerically by generating enough traces of random reaction events. T…
Time SeriesTime Series AnalysisForecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stat…
Time Series