文件列表:

AAPL_974.csv (70461, 2020-05-09)
arima.py (2148, 2020-05-09)
gru_bayesian.py (11903, 2020-05-09)
lstm_basic.py (8829, 2020-05-09)
lstm_bayesian.py (11906, 2020-05-09)
该压缩包的网址.txt (50, 2020-07-22)

# Stock-Market-Prediction This project deals with predicting the opening BID, ASK and PRICE using daily Apple stock data of 4 years. This data would be useful for traders to determine if a stock should be traded as a future, forward, option or some other derivative as they would be able to see the future trend. ## Data Pre-processing and Feature Engineering 1. Check for missing values - no missing values 2. Check if categorical data, encoding needed - no categorical data 3. Check Feature Importance - All features had high significance, so not dropping 4. Split data into train, validation and test in the ratio 60:20:20 5. Feature Scaling - We are normalizing data between zero and one. ## Prediction Models 1. Autoregressive Integrated Moving Average models (ARIMA) 2. Long Short-Term Memory (LSTM) 3. Gated Recurrent Unit (GRU) ## Model Optimization Bayesian Optimization of GRU and LSTM models. ## Accuracy Measures for Comparison 1. Mean Absolute Error (MAE) 2. Root Mean Squared Error (RMSE) 3. Mean Forecast Error (MFE)  Even the less complex models like ARIMA can show better performance than the complex neural netwrok models on some datasets.

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