We derive solutions for the problem of missing and noisy data in nonlinear timeseries prediction from a probabilistic point of view. We discuss different approximations to the so...
This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nea...
Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasti...
In this paper we present a technique for prediction of electrical demand based on multiple models. The multiple models are composed by several local models, each one describing a r...
J. Jesus Rico Melgoza, Juan J. Flores, Constantino...
In this paper we firstly analysis the chaotic characters of three sets of the financial time series (Hang Sheng Index (HIS), Shanghai Stock Index and US gold price) based on the ph...