Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence

Bayesian Time Series Modelling and Prediction with Long-Range Dependence

We present a class of models for trend plus stationary component time series, in which the spectral densities of stationary components are represented via non-parametric smoothness priors combined with long-range dependence components. We discuss model tting and computational issues underlying Bayesian inference under such models, and provide illustration in studies of a climatological time ser...

Wavelet-based Forecasting of Short and Long Memory Time Series

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant “à trous” wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its...

Optimal Deseasonalization for Monthly and Daily Geophysical Time Series

Deseasonalized geophysical time series are often used in time series models (Hipel and McLeod 1994). In this article an optimal method for selecting the deseasonalization transformation is suggested and an R package implementation (McLeod and Gweon 2012) is discussed. Our deseasonalization method may be used with the recently developed periodic autoregression model for daily river flow suggeste...

Long-range Dependence in Daily Stock Volatilities

Recent empirical studies show that the squares of high-frequency stock returns are long-range dependent and can be modeled as fractionally integrated processes, using, for example, long-memory stochastic volatility models. Are such long-range dependencies common among stocks? Are they caused by the same sources of variation? In this paper, we classify daily stock returns of S&P 500 companies on...

Long Range Dependence Eeects and Arch Modelling