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Developing a deep learning algorithm to improve the accuracy of stock price prediction
1. Data collection
The most important step for stock price prediction is data collection. Various data such as historical stock price data, trading volume, corporate financial information, and economic indicators should be collected and utilized for model training.
2. Data preprocessing
Data preprocessing is necessary because the collected data can be noisy. In the case of stock price data, it is a time series data, so you need to perform logarithmic transformation or normalization of stock prices to consider the inferred trend of stock prices.
3. Build a model
You need to build a deep learning model for stock price prediction. You can utilize recurrent neural networks such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit) to learn and predict time series data. You can also use CNN-LSTM, which is a model that combines CNN (Convolutional Neural Network) and LSTM.
4. Hyperparameter tuning
Before training the model, you need to find the optimal hyperparameter values through hyperparameter tuning. You can get optimal performance by adjusting various hyperparameters such as learning rate, batch size, number of hidden layers, and number of nodes.
5. Model training and validation
The collected data is divided into training data and validation data.
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