Higher-order organization of multivariate time series

Time Series Analysis by Higher Order Crossings

Modelling Multivariate Time Series

Multivariate time series (MTS) data are widely available in di erent elds including medicine, nance, science and engineering. Modelling MTS data e ectively is important for many decision-making activities. In this paper, we will describe some of our e orts in modelling these data for numerous tasks such as outlier analysis, forecasting and explanation. Through the analysis of various kinds of M...

Artificial Higher Order Neural Networks in Time Series Prediction

Real world problems are described by nonlinear and chaotic processes, which makes them hard to model and predict. This chapter first compares the neural network (NN) and the artificial higher order neural network (HONN) and then presents commonly known neural network architectures and a number of HONN architectures. The time series prediction problem is formulated as a system identification pro...

Higher order neural networks for financial time series prediction

Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share...

Modelling of Multivariate Time Series

We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependencies. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs ...