Updating Yealink model series

Nonparametric time series forecasting with dynamic updating

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univaria...

Image-Based Model Updating

This paper presents a novel image-based approach for updating the geometry of 3D models. The technique can cope with large-scale models, using a single imaging sensor to which an arbitrary motion is applied. Current approaches usually do not fully take advantage of strong prior information, often available in the form of an initial model. The approach is thus novel in that geometric anomalies a...

A model updating strategy for predicting time series with seasonal patterns

Traditional methodologies for Q1 time series prediction take the series to be predicted and split it into training, validation, and test sets. The first one serves to construct forecasting models, the second set for model selection, and the third one is used to evaluate the final model. Different time series approaches such as ARIMA and exponential smoothing, as well as regression techniques su...

THE MODEL UPDATING OF MASS, DAMPING AND STIFFNESS MATRICES

Eigenspace Merging for Model Updating

The Karhunen-Loève transform (KLT) is an optimal method for dimensionality reduction, widely applied in image compression, reconstruction and retrieval, pattern recognition and classification. The basic idea consists in evaluating, starting from a set of representative examples, a reduced space, which takes into account the structure of the data distribution as much as possible, and representin...