Forecasting in multivariate irregularly sampled time series with missing values

Abstract

Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series. In this work, we present an approach to forecast not only the values but also the time at which they are expected to occur.

arXiv admin note: text overlap with arXiv:1905.12374 by other authors

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Latent ODEs for Irregularly-Sampled Time Series

Cited by in corpus (1)

• A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data