A time series of potentially noisy, partially-measured values of a physical process is represented as a non-overlapping matrix. For several classes of common model functions, it can be proved that the resulting matrix has a low rank or approximately low rank, allowing a matrix estimation ...
Parameter estimation depends on information content of process data segments. Selection of highly informative data needs multivariate time-series segmentation tool. Fisher information based time-series segmentation framework is developed. Segments with parameter-set specific information content can be segregated...
Recently I began to look further into Time Series(TS). During the course of my Master’s degree, I used the forecast package quite a bit (Thanks to Prof. Hyndman), and TS got my attention. So, after reading lots of publications about everything you can i
Many practitioners then choose to base their inferences on such an estimator. The usual practice is to couple it with an asymptotically valid estimation of its covariance matrix, and then carry out hypothesis tests that are valid under heteroskedasticity of unknown form. We use numerical integration...
Moreover, it is also hard to relate the estimation error to the measurement matrix. Third, the algorithms work well for certain types of sparse vectors, but not all. For example, the performance of the basis pursuit algorithm, which is based on ℓ1 relaxation, is independent of the ...
The current data are input into the program module of the extended Kalman filtering algorithm based on the model, and the initial values of other parameters have been determined in the program. The program finally outputs the ampere hour time integral and the state estimation curve of the extende...
Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing...
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python pythonmachine-learningstatisticstime-seriessignal-processingimage-processingeegremote-sensingbrain-computer-interfacecovariance-matrixpositive-definite-matricescovariance-estimationriemannian-geometryradar-imagesym...
The sparse matrix approaches to computing the log-determinant term discussed in the context of maximum likelihood estimation can be applied to MCMC estimation of the Bayesian variant of these models, making them relatively fast. View chapterExplore book Related terms: Support Vector Machine ...
This paper provides a framework for real-time estimation/prediction of time-dependent Origin-Destination (O-D) matrices. A case study is described in which the model was implemented using data for the Massachusetts Turnpike.关键词: Advanced traffic management systems Real time information Traffic esti...