Vector double autoregressive modelquasi maximum likelihood estimatorMotivated by the double autoregressive (DAR) model, in this paper, we study a vector double autoregressive model (VDAR). The model is a straightforward extension from univariate case to multivariate case. Sufficient ergodicity conditions ...
Motivated by the double autoregressive (DAR) model, in this talk, we study a vector double autoregressive model (VDAR). The model is a straightforward extension from univariate case to multivariate case. Sufficient ergodicity conditions are given for the model. Without existence of second moment co...
Compared to the Log-Mean Divisia Index (LMDI) (Li et al., 2023b), Vector Autoregressive model (VAR) (Li, 2022) and Spatial Dubin Model (SDM) (Zhang and Deng, 2022) used in existing studies, the GWR method can effectively analyze the spatial heterogeneity of the relationship between ...
ON THE PREDICTIVE POWER OF INTEREST RATE SPREADS: A VECTOR AUTOREGRESSIVE APPROACH International Journal of FinanceParhizgari, A. M.Welch, W.Aburachis, A.
24-01-16 VQ-TR ICLR 2024 VQ-TR: Vector Quantized Attention for Time Series Forecasting None 24-01-22 HDformer Arxiv 2024 The Bigger the Better? Rethinking the Effective Model Scale in Long-term Time Series Forecasting None 24-02-04 Minusformer Arxiv 2024 Minusformer: Improving Time Series...
Day-ahead electricity price forecasting based on rolling time series and least square-support vector machine model day-ahead electricity market clearing price forecastinggeneralized autoregressive conditional heteroskedasticity modelConsidering the electricity price's volatility and ... J Zhang,J Han,R Wang,...
Double sampling (x)over-bar control chart for a first-order autoregressive moving average process model In this paper, we consider the double sampling (DS) (X) over bar control chart for monitoring processes in which the observations can be represented as a first-order autoregressive moving avera...
the final feature maps go through a fully connected (dense) layer to extract high-level features. For regression problems, the extracted features can be combined in a prediction mechanism with an activation function or a supervised learning model (e.g., support vector regression) to estimate the...
The paper deals with a linear model with linear variance-covariance structure, where the linear function of the parameter of expectation is to be estimated. The two-stage estimator is based on the observation of the vector $Y$ and on the invariant quadratic estimator of the variance-covariance ...
[18] built a short-term wind speed prediction model based on least squares support vector machines (LSSVM). The prediction results of the linear kernel, Gaussian kernel, and polynomial kernel are compared, and the comparison results prove that the prediction effects of the three kernel functions...