A network autoregressive model with GARCH effects and its applications. [PDF]
PLoS One, 2021 Huang SF, Chiang HH, Lin YJ.
europepmc +1 more source
An Algorithm for Parameters Estimation of Autoregressive Model of Basic Speech Units
, 2015 Иван Вадимович Губочкин
openalex +2 more sources
When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns
Journal of Forecasting, EarlyView.ABSTRACT We study how statistical forecast gains for Bitcoin translate into trading profits. Using real‐time out‐of‐sample forecasts from daily bivariate VARs from October 2021 to February 2024, we show that Bitcoin returns are forecastable and that seven predictive indices yield significant gains in directional accuracy (DA).
Rehan Arain, Stephen Snudden
wiley +1 more source
Spatial-temporal analysis of cerebral infarction mortality in Hokkaido, Japan: an ecological study using a conditional autoregressive model. [PDF]
Int J Health Geogr, 2022 Ohashi K +9 more
europepmc +1 more source
An Empirical Analysis of the Effects of Population Growth on Economic Growth in Ethiopia Using an Autoregressive Distributive Lag (ARDL) Model Approach [PDF]
, 2022 Alemayehu Temesgen Befikadu +1 more
openalex +1 more source
Mortality Forecasting Using Variational Inference
Journal of Forecasting, EarlyView.ABSTRACT This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two‐step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use.
Patrik Andersson, Mathias Lindholm
wiley +1 more source
Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data. [PDF]
J Healthc Eng, 2022 Daniyal M +3 more
europepmc +1 more source
LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding [PDF]
Doohyuk Jang +7 more
openalex +1 more source
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, EarlyView.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$$ c $$ 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