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Lilian Weng's tutorial series explains RNNs for stock price prediction

By PulseAugur Editorial · [2 sources] ·

Lilian Weng's blog posts detail the construction of a recurrent neural network (RNN) using TensorFlow for stock price prediction. The first part focuses on building a basic RNN with LSTM cells to predict S&P 500 closing prices using historical data from Yahoo! Finance. The second part extends this model to handle multiple stocks by incorporating stock symbol embeddings as input, allowing the network to differentiate patterns across various price sequences. AI

RANK_REASON Blog posts detailing the technical implementation of an AI model for a specific task.

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Lilian Weng's tutorial series explains RNNs for stock price prediction

COVERAGE [2]

  1. Lil'Log (Lilian Weng) TIER_1 English(EN) ·

    Predict Stock Prices Using RNN: Part 2

    <!-- This post is a continued tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. Part 2 attempts to predict prices of multiple stocks using embeddings. The full working code is available in [lilianweng/stock-rnn](https://github.c…

  2. Lil'Log (Lilian Weng) TIER_1 English(EN) ·

    Predict Stock Prices Using RNN: Part 1

    <!-- This post is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. Part 1 focuses on the prediction of S&P 500 index. The full working code is available in [lilianweng/stock-rnn](https://github.com/lilianweng/stock-rnn). --> …

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