Results 111 to 120 of about 4,998 (266)
The Accuracy Smoothness Dilemma in Prediction: A Novel Multivariate M‐SSA Forecast Approach
Journal of Time Series Analysis, EarlyView.ABSTRACT Forecasting presents a complex estimation challenge, as it involves balancing multiple, often conflicting, priorities and objectives. Conventional forecast optimization methods typically emphasize a single metric, such as minimizing the mean squared error (MSE), which may neglect other crucial aspects of predictive performance. To address this
Marc Wildi
wiley +1 more source
Using Subspace Methods for Estimating ARMA Models for Multivariate Time Series with Conditionally Heteroskedastic Innovations [PDF]
This paper deals with the estimation of linear dynamic models of the ARMA type for the conditional mean for time series with conditionally heteroskedastic innovation process widely used in modelling financial time series.
Dietmar Bauer
core
Marchenko–Pastur Laws for Daniell Smoothed Periodograms
Journal of Time Series Analysis, EarlyView.ABSTRACT Given a sample X0,…,Xn−1$$ {X}_0,\dots, {X}_{n-1} $$ from a d$$ d $$‐dimensional stationary time series (Xt)t∈ℤ$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$, the most commonly used estimator for the spectral density matrix F(θ)$$ F\left(\theta \right) $$ at a given frequency θ∈[0,2π)$$ \theta \in \left[0,2\pi \right) $$ is the Daniell smoothed ...
Ben Deitmar
wiley +1 more source
A note on non-negative ARMA processes
, 2005 . Recently, there are much works on developing models suit-able for analyzing the volatility of a discrete-time process. Within the framework of Auto-Regressive Moving-Average (ARMA) processes, we de-rive a necessary and sufficient condition for the ...
K. S. Chan, Henghsiu Tsai
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On Testing for Independence Between Generalized Error Models of Several Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi +2 more
wiley +1 more source
KALMAN FILTERS AND ARMA MODELS
Ratio Mathematica, 2003 The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem for time series. After a quite general formulation of the prediction problem, the contributions of its solution by the great mathematicians Kolmogorov and
Aniello Fedullo
doaj
Penalized Convex Estimation in Dynamic Location Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley +1 more source
Continuous-time fractional ARMA processes
The field of discrete-time fractional ARMA processes is now of longstanding interest. However, to the best of the author's knowledge, continuous time fractional ARMA processes have not yet been defined.
Deniau, C., Oppenheim, G., Viano, M. C.
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Ecological and genomic variation in ectomycorrhizal fungal exploration types
New Phytologist, EarlyView.Summary Ectomycorrhizal fungi (EMF) produce mycelia with variable extension and complexity, which can be classified according to soil ‘exploration types’ (ETs). ETs have received attention as one of the few mycorrhizal trait frameworks, but without an empirical classification of ET functional diversity and environmental preferences, understanding and ...
Thomas M. Mansfield +55 more
wiley +1 more source
FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS [PDF]
In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes.
Rafal Weron, Adam Misiorek
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