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Time series database, Time series analysis, Time series, Singular spectrum analysis, Statistics, SARSA

Generating Synthetic Sequential Data Using GANs

On Sep 24, 2020
@thinkmariya shared
Generative models for sequential data and time-series usually result in relatively poor synthetic data quality and low flexibility. This article addresses these shortcomings by describing and applying an extended version of DoppelGANger. #AI #GAN https://t.co/OCGTcAe71b
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Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. But privacy regulations limit and dramatically slow-down access to useful data, essential to research and development. ...

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On Sep 24, 2020
@thinkmariya shared
Generative models for sequential data and time-series usually result in relatively poor synthetic data quality and low flexibility. This article addresses these shortcomings by describing and applying an extended version of DoppelGANger. #AI #GAN https://t.co/OCGTcAe71b
Open

Generating Synthetic Sequential Data Using GANs

Generating Synthetic Sequential Data Using GANs

Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. But privacy ...

3 reasons to add deep learning to your time series toolkit

3 reasons to add deep learning to your time series toolkit

The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.

Tutorial: Time Series Analysis with Pandas

Tutorial: Time Series Analysis with Pandas

Learn about powerful time series tools in the pandas library and get hands-on programming some time series analysis in Python in this data science tutorial.

ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data

ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data

Motivation There are five types of traditional time series models most commonly used in epidemic time series forecasting, which includes Autoregressive (AR),…

Multi-Step Time Series Generator for Molecular Dynamics

Multi-Step Time Series Generator for Molecular Dynamics

Using the advantages of feature extraction and step skip, our model efficiently generates MD time series data. Training pro- cess of this network is as follows: (1) Gz is trained to min- ...

Time Series Database Vs. Common Database Technologies for IoT

Time Series Database Vs. Common Database Technologies for IoT

In this post, we compare the difference between a time series database and other common database technologies used for IoT applications.

Deep Learning track

Deep Learning track

Here we provide a list of topics covered by the Data Science track, split into methods and computational aspects. The ordering of topics does not reflect the order in which they will be ...

Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation

Anticipation-RNN: enforcing unary constraints in sequence generation, with application to interactive music generation

Recurrent neural networks (RNNs) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. ...