AbstrAct The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that ...

We consider an adversarial formulation of the problem of predicting a time series with square loss. The aim is to predict an arbitrary sequence of vectors almost as well as the best smooth comparator sequence in retrospect. Our approach allows natural measures of smoothness such as the squared norm of increments. More generally, we consider a linear time series model and penalize the comparator...

The Artificial Neural Network (ANN) is a computer technique that uses mathematical model to represent simpler form of the biologic neural structure. It formed by many processing units and its intelligent behavior comes from iterations between these units. One application ANN for time series prediction algorithms, where network learns dependent data it able predict future values. In this work, a...

The paper presents the results of simulations performed for predictive goals for the main Polish index named WIG20, using the historical quotes on several connected financial time series. The data (monthly and daily tested) used to predict WIG20 are such series as economical supply of money, level of unemployment, inflation and lagged series of the main index. In order to reach prediction goal,...

this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...