Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

01/31/2021
by   Longyuan Li, et al.
0

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

01/31/2021

Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting

Probabilistic time series forecasting involves estimating the distributi...
01/08/2021

Long Horizon Forecasting With Temporal Point Processes

In recent years, marked temporal point processes (MTPPs) have emerged as...
11/29/2017

A Multi-Horizon Quantile Recurrent Forecaster

We propose a framework for general probabilistic multi-step time series ...
02/23/2016

Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series

Approximate variational inference has shown to be a powerful tool for mo...
06/03/2021

Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems

The demand of probabilistic time series forecasting has been recently ra...
04/03/2021

COHORTNEY: Deep Clustering for Heterogeneous Event Sequences

There is emerging attention towards working with event sequences. In par...
05/24/2017

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiqu...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.