Forecasting of Carbon Price Based on Boosting-ARMA Model
, 2017 自回归滑动平均(ARMA)模型是最流行的预测模型之一,而模型选择却是使用ARMA进行预测的难点,尤其是当真实模型的阶数较高时,因此提出Boosting-ARMA预测算法,利用Boosting算法进行最优子集ARMA寻找,自动且高效地完成ARMA模型的识别。模拟实验显示,Boosting-ARMA优于其他方法,用新算法预测碳价实证分析发现,Boosting-ARMA算法可以获得较高的碳价预测准确性并且方便快捷。Autoregressive moving average （ARMA） model is ...
王娜
core +1 more source
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
ARMA Modelling for Whispered Speech
Journal of Measurement Science and Instrumentation, 2010 The Autoregressive Moving Average（ARMA）model for whispered speech is proposed.Compared with normal speech,whispered speech has no fundamental frequency because of the glottis being semi-opened and turbulent flow being created,and formant shifting exists ...
Xue-li LI, Wei-dong ZHOU
doaj
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
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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
Bitcoin Return Dynamics Volatility and Time Series Forecasting
International Journal of Financial StudiesBitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the ...
Punit Anand, Anand Mohan Sharan
doaj +1 more source
Numerical simulations of sanding under different stress regimes
, 2012 Laboratory experiments of sand production conducted under true-triaxial stress conditions were simulated numerically using ABAQUS program. The experiments were performed in a true-triaxial stress cell on 100×100×100 mm3 cubes of synthetic sandstones. Two
Rasouli, Vamegh +2 more
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Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
Journal of Time Series Analysis, EarlyView.ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley +1 more source
Generalized normal ARMA model applied to the areas of economy, hydrology, and public policy
openIn questo studio viene presentata una nuova classe di modelli per serie temporali, il modello autoregressivo a media mobile con distribuzione normale generalizzata (GN-ARMA).
XEKA, ARMANDO
core
Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts [PDF]
Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data.
Ramirez, Octavio A.
core