Time series deals with sequential data where the data is indexed (ordered) by a time dimension.
( Image credit: Autoregressive CNNs for Asynchronous Time Series )
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In this paper, the generalized regression neural network is used to predict the GNSS position time series.
First, we propose a novel Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that significantly improves accuracy and training time over state-of-the-art DA strategies on real-world sensor data benchmarks.
We demonstrate that our sequential fusion approach is superior to methods that directly project all the data into the most recent viewpoint.
In particular, we investigate the effectiveness of several forms of data binning, i. e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models.
An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction.
(ii)~can Neural ODEs solve the irregular sampling rate challenge of existing neural network models for a continuous time series, i. e., length and dynamic nature, (iii)~how to reduce the training and evaluation time of existing Neural ODE systems?
Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field.
Practical applications, e. g., sensor data, healthcare, weather, generates data that is in truth continuous in time, and informative missingness is a common phenomenon in these datasets.
Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine.