Results 61 to 70 of about 4,760 (217)
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
Fractional Gaussian Noise: Spectral Density and Estimation Methods
The fractional Brownian motion (fBm) process, governed by a fractional parameter H∈(0,1)$$ H\in \left(0,1\right) $$, is a continuous‐time Gaussian process with its increment being the fractional Gaussian noise (fGn). This article first provides a computationally feasible expression for the spectral density of fGn.
Shuping Shi, Jun Yu, Chen Zhang
wiley +1 more source
Wavelet based long memory model for modelling wheat price in India
Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year.
RANJIT KUMAR PAUL +2 more
doaj +1 more source
S&P 500 microstructure noise components: empirical inferences from futures and ETF prices
By studying the differences between futures prices and exchange‐traded fund prices for the S&P 500 index, original results are obtained about the distribution and persistence of the microstructure noise component created by positive bid‐ask spreads and discrete price scales.
Stephen J. Taylor
wiley +1 more source
Local powers of least‐squares‐based test for panel fractional Ornstein–Uhlenbeck process
In recent years, significant advancements have been made in the field of identifying financial asset price bubbles, particularly through the development of time‐series unit‐root tests featuring fractionally integrated errors and panel unit‐root tests.
Katsuto Tanaka, Weilin Xiao, Jun Yu
wiley +1 more source
Forecasting cryptocurrency prices time series using machine learning approach [PDF]
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and ...
Derbentsev Vasily +3 more
doaj +1 more source
Forecasting Digital Asset Return: An Application of Machine Learning Model
ABSTRACT In this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable.
Vito Ciciretti +4 more
wiley +1 more source
The task of forecasting the dynamics of changes in the rates of financial instruments is relevant, since its solution would reduce risks and increase the profitability of operations in financial markets.
Pyotr Mikhailovich Simonov +1 more
doaj +1 more source
Local Whittle estimation in time‐varying long memory series
The memory parameter is usually assumed to be constant in traditional long memory time series. We relax this restriction by considering the memory a time‐varying function that depends on a finite number of parameters. A time‐varying Local Whittle estimator of these parameters, and hence of the memory function, is proposed.
Josu Arteche, Luis F. Martins
wiley +1 more source
Improved Trend Analysis With EOFs and Application to Warming of Polar Regions
Introducing a variation of EOF analysis, we obtain an insignificant Antarctic trend between 1979 and 2023 of (0.13 ± 0.17) K/decade. The first principal component completely captures the trend for land regions of the order of the size of most countries.
Ewan T. Phillips, Holger Kantz
wiley +1 more source

