Forecasting with Nonlinear Time Series Models

Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

Forecasting with Unobserved Components Time Series Models

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for ‘nowcasting’ . The structural interpretation allows extensions to classes of models that are able to deal with various issues in ...

Forecasting with Periodic Autoregressive Time Series Models

This chapter is concerned with forecasting univariate seasonal time series data using periodic autoregressive models We show how one should account for unit roots and deterministic terms when generating out of sample forecasts We illus trate the models for various quarterly UK consumption series This is the rst version July of a chapter that is to be prepared for potential inclusion in the Comp...

Nonlinear Time Series in Financial Forecasting

Time Series Forecasting: a Nonlinear Dynamics Approach

The problem of prediction of a given time series is examined on the basis of recent nonlinear dynamics theories. Particular attention is devoted to forecast the amplitude and phase of one of the most common solar indicator activity, the international monthly smoothed sunspot number. It is well known that the solar cycle is very difficult to predict due to the intrinsic complexity of the related...