Results 101 to 110 of about 12,105 (264)

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes [PDF]

Nonlinear Processes in Geophysics, 2005
Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average ...
W. Wang, W. Wang, P. H. A. J. M Van Gelder, J. K. Vrijling, J. Ma   +4 more
doaj  

Research on the prediction of dangerous goods accidents during highway transportation based on the ARMA model. [PDF]

J Loss Prev Process Ind, 2021
Li X, Liu Y, Fan L, Shi S, Zhang T, Qi M.   +5 more
europepmc   +1 more source

Modeling and forecasting electricity loads: A comparison [PDF]

In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components – a deterministic (representing seasonalities) and a stochastic (representing noise).
Rafal Weron, Adam Misiorek
core  

A Conditional Tail Expectation Type Risk Measure for Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We consider the estimation of the conditional expectation 𝔼(Xh|X0>UX(1/p)), provided 𝔼|X0|<∞, at extreme levels, where (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$ is a strictly stationary time series, UX$$ {U}_X $$ its tail quantile function, h$$ h $$ is a positive integer and p∈(0,1)$$ p\in \left(0,1\right) $$ is such that p→0$$ p\to ...
Yuri Goegebeur, Armelle Guillou, Jing Qin   +2 more
wiley   +1 more source

Functional Vašiček Model

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose a new formulation of the Vašičekmodel within the framework of functional data analysis. We treat observations (continuous‐time rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka, Neda Mohammadi, Leonardo Santoro, Hong Miao, Haonan Wang   +4 more
wiley   +1 more source

The Application of Spatial Analysis and Time Series in Modeling the Frequency of Earthquake Events in Bengkulu Province

Aceh International Journal of Science and Technology, 2018
This study provides an overview in combining spatial analysis and time series analysis to model the frequency of earthquake. The aim of this research is to apply the spatial statistical analysis and time series analysis in estimating semivariogram ...
Fachri Faisal, Pepi Novianti, Jose Rizal
doaj   +1 more source

Applying articial neural network techniques to the ARMA model

, 2015
ARMA models and Artificial Neural Networks are commonly used approaches for forecasting timeseries. ARMA models are accurate and efficient, but difficult to use and inflexible to implement. Artificial Neural Networks are less efficient, but more flexible
Nathan Rose (17650245)
core   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

Robust CDF‐Filtering of a Location Parameter

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania, Andrew C. Harvey, Alessandra Luati   +2 more
wiley   +1 more source

Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley   +1 more source