Forecasting Stock Prices with PyTorch
The article explores the application of PyTorch and Recurrent Neural Network (RNN) architectures, Long short-term memory (LSTM) and Gated recurrent units (GRU), for predicting Amazon’s stock prices.
The prediction process relies on time series forecasting, which predicts future values based on historical data. After the initial data preprocessing, the models were trained over 100 epochs, revealing similar performances. However, when predicting on the test set, the GRU model outperformed the LSTM in terms of accuracy and processing speed. The author concludes that despite their good training performances, both models stagnate around the 40th epoch, indicating a lower number of epochs may be sufficient. The GRU’s superior performance was anticipated due to its less complex structure and fewer parameters, offering similar accuracy but at a higher speed than the LSTM model.
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