Results 81 to 90 of about 6,621,438 (152)
Testing for threshold effect in ARFIMA models: Application to US unemployment rate data
Macroeconomic time series often involve a threshold effect in their ARMA representation, and exhibit long memory features. In this paper we introduce a new class of threshold ARFIMA models to account for this.
Scaillet, Olivier, Lahiani, A.
core +2 more sources
Bayesian Analysis of Long Memory and Persistence using ARFIMA Models
This paper provides a Bayesian analysis of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We discuss in detail inference on impulse responses, and show how Bayesian methods can be used to (i) test ARFIMA models against ARIMA ...
Gary Koop +3 more
core +1 more source
SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction. [PDF]
Yan W +5 more
europepmc +1 more source
Comparing the bias and misspecification in ARFIMA models
ARFIMA models - AutoRegressive Fractional Integrated Moving Average modelsSIGLEGBUnited ...
Smith, J. +3 more
core
The thesis deal with long-memory processes which are defined by several ways. The main concern is dedicated to ARFIMA model, to its basic properties and its application.
Vdovičenko, Martin
core
Stationarity is a fundamental assumption in time series modeling that underlies reliable statistical inference and forecasting. Time series data can be found in many domains, including industry, engineering, finance, economics, epidemiology, and health ...
Apollinaire BATOURE BAMANA +3 more
doaj +1 more source
South African inflation modelling using bootstrapped long short-term memory methods. [PDF]
Kubheka S.
europepmc +1 more source
Time Analysis of an Emergent Infection Spread Among Healthcare Workers: Lessons Learned from Early Wave of SARS-CoV-2. [PDF]
Leme PAF +8 more
europepmc +1 more source
Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For? [PDF]
Perquin MN +3 more
europepmc +1 more source
Forecasting commodity prices: empirical evidence using deep learning tools. [PDF]
Ben Ameur H +4 more
europepmc +1 more source

