Nonlinear time-series modeling and prediction using correlation analysis

Tidal prediction using time series analysis of Buoy observations

Although tidal observations which are extracted from coastal tide gages, have higher accuracy due to their higher sampling rate, installing these types of gages can impose some spatial limitation since we cannot use every part of sea to install them. To solve this limitation, we can employ satellite altimetry observations. However, satellite altimetry observations have lower sampling rate. Acco...

Modeling and prediction of time-series of monthly copper prices

One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...

Dynamical Modeling with Kernels for Nonlinear Time Series Prediction

We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameters of the model, and second, to compute preimages of the time series predicted in the feature space by means of Support Vector Regression. Our model shows ...

Fractal Based Analysis, Modeling and Prediction of Neurophysiological Time Series

Neurophysiology is a branch of physiology and neuroscience that is concerned with the study of the functioning of the nervous system. Neurophysiological time series are signal shaped time series which are governed from different processes that are related to nervous system. The most important neurophysiological time series which is directly related to the activity of the nervous system is the E...

Nonlinear Time Series Analysis