Bag-of-Temporal-SIFT-Words for Time Series Classification

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA- Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from

Specifically, in order to be applied to time series data, these methods have to be adapted to consider the interactions in time beyond those in space: the

In this paper, we address this gap and investigate and compare the predictive performance of alternative linear and non-linear time series methods (seasonal ARIMA, Holt-Winters

In this paper, we address this gap and investigate and compare the predictive power of alternative linear and non-linear time series methods (seasonal ARIMA, Holt-Winters and

Because there is still a relevant number of classification modifications over time, LSTM com- prove that its capabilities for this type of data makes it better than the

Averaging empirical variograms (semivariances) (AEV), which are computed by paired comparisons in individual time steps, over each spatial lag within a pooled time series represents

Recently, the problem of detecting novelties in time series has received great attention, with a number of different techniques being proposed and studied, including techniques based

For the burnt area classification, we initially used the Fourier harmonic model to define outliers in the time series that represented pixels of possible burnt areas and, then,