Autoregressive fractionally integrated moving average ARFIMA pro cesses are widely used for modeling time series exhibiting both long memory and short memory behavior Properties of Toeplitz matrices associated with the spectral density functions of Gaussian ARFIMA processes are used to compute di erential geometric quantities INTRODUCTION Time series data occurring in several areas such as geol...

Purpose The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns volatility. Design/methodology/approach competing models are autoregressive moving average (ARMA) model fractional integrated (ARFIMA) for returns. For volatility, exponential generalized conditional heteroscedasticity (EGARCH) with GARCH (FIGARCH) component...

The peaks-over-threshold (POT) method has a long tradition in modelling extremes environmental variables. However, it originally been introduced under the assumption of independently and identically distributed (iid) data. Since data often exhibits time series structure, this is likely to be violated due short- long-term dependencies practical settings, leading clustering high-threshold exceeda...

This paper considers the application of long memory processes to describe inflation with seasonal behaviour. We use three different long memory models taking into account the seasonal pattern in the data. Namely, the ARFIMA model with deterministic seasonality, the ARFISMA model, and the periodic ARFIMA (PARFIMA) model. These models are used to describe the inflation rates of four different cou...

Son yıllarda rüzgâr enerjisinin yenilenebilir bir enerji kaynağı olarak yaygınlaşması ile birlikte hızının üretimindeki ekonomik etkilerinin değerlendirilmesi de önem kazanmış ve planlamalarında doğru hızı tahmini modellemesine olan ilgi artmıştır. Çalışmada klasik yaklaşımlardan farklı hızlarındaki uzun hafıza özelliği incelenmiştir. Bu amaçla, Türkiye’ Bartın ili Amasra bölgesi hızları için e...

We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm combines the Durbin-Levinson and Viterbi procedures. A Monte Carlo experiment reveals that the finite s...

By design a wavelet's strength rests in its ability to localize a process simultaneously in time-scale space. The wavelet's ability to localize a time series in time-scale space directly leads to the computational e ciency of the wavelet representation of a N N matrix operator by allowing the N largest elements of the wavelet represented operator to represent the matrix operator [Devore, et al....