Interpretable time series kernel analytics by pre-image estimation
نویسندگان
چکیده
منابع مشابه
Geovisual analytics of Satellite Image Time Series
Satellite image time series provide valuable information on the Earth’s dynamics at a variety of spatio-temporal scales. Progress on information and communication technologies has greatly improved the access to such time series. For instance, the GEONETCast system freely distributes near real time raw satellite images and higher level products to end-users all over the word. This explains the u...
متن کاملDistributed Time Series Analytics
In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitoring, finance, IoT applications, news feeds, social networks, data centre monitoring and so on. Availability of such large scale time series d...
متن کاملDiscrimination of time series based on kernel method
Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than ...
متن کاملInterpretable Categorization of Heterogeneous Time Series Data
The explanation of heterogeneous multivariate time series data is a central problem in many applications. The problem requires two major data mining challenges to be addressed simultaneously: Learning models that are humaninterpretable and mining of heterogeneous multivariate time series data. The intersection of these two areas is not adequately explored in the existing literature. To address ...
متن کاملKernel estimation for time series: An asymptotic theory
We consider kernel density and regression estimation for a wide class of nonlinear time series models. Asymptotic normality and uniform rates of convergence of kernel estimators are established under mild regularity conditions. Our theory is developed under the new framework of predictive dependence measures which are directly based on the data-generating mechanisms of the underlying processes....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2020
ISSN: 0004-3702
DOI: 10.1016/j.artint.2020.103342