Results 51 to 60 of about 7,382 (224)
Mathematics, 2021 The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data
Pushpa Dissanayake +3 more
doaj +1 more source
Combining long memory and level shifts in modeling and forecasting the volatility of asset returns [PDF]
, 2015 We propose a parametric state space model of asset return volatility with an accompanying estimation and forecasting framework that allows for ARFIMA dynamics, random level shifts and measurement errors.
Perron, Pierre, Varneskov, Rasmus T.
core
Jurnal Gaussian, 2021 Time series data is a type of data that is often used to estimate future values. Long memory phenomenon often occurs in time series data. Long memory is a condition that shows a strong correlation between observations even though they are quite far away.
Rezky Dwi Hanifa +2 more
semanticscholar +1 more source
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, Volume 45, Issue 3, Page 1261-1291, April 2026.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
Stock-return volatility persistence over short and long range horizons: Some empirical evidences
Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, 2020 In this paper, we account for memory failure or otherwise in the daily evolution of stock return and volatility within the purview of short and long ranges based on the arrival of fundamental news.
Kolawole Subair, Ajibola Arewa
doaj +1 more source
Efficient Bayesian inference for ARFIMA processes [PDF]
, 2015 Abstract. Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is
Graves, T. +3 more
openaire +2 more sources
Using Fuzzy-ARFIMA Models to Predict Births in Basra Governorate
Journal of Physics: Conference Series, 2021 Today’s time series analysis is one of the most important statistical methods in forecasting, and it has been used in many economic, industrial, commercial and science fields, by representing time series characterized by long-term memory that helps ...
Raissan A. Zalan, Zainab Sami Yaseen
semanticscholar +1 more source
Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.In this paper, we model edge traffic with a conformable fractional partial differential equation that keeps memory in time and space. The solution represents a unit‐free attack pressure, built from a z‐scored edge series, a quiet period baseline, and a partially absorbing boundary that reflects scrubbing and rate limits.
Ahmad Alshanty +3 more
wiley +1 more source
, 2016 In this paper, we show that the central limit theorem (CLT) satisfied by the data-driven Multidimensional Increment Ratio (MIR) estimator of the memory parameter d established in Bardet and Dola (2012) for d $\in$ (--0.5, 0.5) can be extended to a ...
Bardet, Jean-Marc, Dola, Béchir
core +2 more sources
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA [PDF]
, 2016 Long memory plays an important role in many fields by determining the behaviour and predictability of systems; for instance, climate, hydrology, finance, networks and DNA sequencing.
Franzke, Christian +3 more
core +3 more sources