Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

Dynamic time war** (DTW) has proven itself to be an exceptionally strong distance
measure for time series. DTW in combination with one-nearest neighbor, one of the simplest …

We propose novel approaches to cast the widely-used family of Dynamic Time War**
(DTW) distances and similarities as positive definite kernels for time series. To this effect, we …

The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

Rapid and accurate crop type map** is of great significance for agricultural management
and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) …

This paper presents a novel method for learning the cyclic contents of stochastic time series:
the deep time-growing neural network (DTGNN). The DTGNN combines supervised and …

In this paper, we propose a novel space-time geometric representation of human landmark
configurations and derive tools for comparison and classification. We model the temporal …

Time series data analytics has been a problem of substantial interests for decades, and
Dynamic Time War** (DTW) has been the most widely adopted technique to measure …

Vegetable production is important because of the food security, diet improvement and socio-
economic value. Map** the location and extent of vegetable fields is therefore important in …

In recent years there has been growing interests in mining time series data. To overcome the
adverse influence of time shift, a number of effective elastic matching approaches such as …