simoninithomas/Time-series-prediction-and-text-generation
Built RNNs that can generate sequences based on input data - with a focus on two applications: used real market data in order to predict future Apple stock prices using an RNN model. The second one will be trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and generates wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel.
This project explores using Recurrent Neural Networks (RNNs) for two distinct applications. It can take historical stock market data to generate predictions for future stock prices, or it can be trained on a large text (like a novel) to produce new, creative sentences in a similar style. Financial analysts could use it to explore potential stock movements, while writers or content creators might find it useful for generating creative text ideas.
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Use this if you are a data science student or an enthusiast looking to learn and implement basic RNNs for either predicting time-series data or generating text.
Not ideal if you need a production-ready, highly accurate financial forecasting tool or a sophisticated natural language generation system for commercial use.
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Oct 16, 2017
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