In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Browse by purposeUnknownAgent skill extensions · Command-line tools
TypeApps & systemsApps & systems
SubcategoryGeneralDeveloper tools
PlatformNot verifiedNot verified
Cost detailsCheck current official software, hosting, model and infrastructure pricing.Verify software, hosting, model and infrastructure costs against the current official documentation.
Pricing source——
First collected2026-11-032026-11-02
MetricRepository stars, not unique users or individual-skill installsRepository Stars, not unique users
LanguageJavaScriptTypeScript
About this projectIn this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.An agent-assisted reverse engineering tool for application behavior and native binaries.
Measured at2026-11-03 08:27 UTC2026-11-02 19:28 UTC
License tiers are derived from metadata. Check obligations for your use; unknown does not mean prohibited. Ranked by repository stars, one position per repository. Stars and forks are not user counts.