Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting

A Polynomial Method for Temporal Disaggregation of Multivariate Time Series

Copula Methods for Forecasting Multivariate Time Series

Copula-based models provide a great deal of ‡exibility in modelling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to ‡exibility, this often also facilitates estimation of the model in stages, reducing the computational burden. Thi...

R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting

Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency compo...

Multivariate Dynamic Kernels for Financial Time Series Forecasting

We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done throu...

Multivariate Time Series Prediction via Temporal Classification

One of the important problems in many process industries is how to predict the occurrence of abnormal situations ahead of time in a multivariate time series environment. For example, in an oil refinery, hundreds of sensors (process variables) are installed at different sections of a process unit. These sensors constantly monitor the development of every stage of the process. Typically, each pro...