International University of Management Stock Price Prediction Program See attached images <<<<<<<<<<<<<<<< Predict Stock Market in the Future This exercise

International University of Management Stock Price Prediction Program See attached images <<<<<<<<<<<<<<<< Predict Stock Market in the Future This exercise is used to replace the constraint-time exam of the BAAI course. You are going to solve a problem of predicting the future stock market with the LSTM model in Keras. O Frist, we need the dataset of stock market, e.g., S&P 500 index from investing.com. This dataset includes 3,590 rows and 7 feature variables. Each row corresponds to a day, and includes the variables: Date: Time Variable Price: S&P 500 index Open: Open price of this date High: The highest price of this date Low: The lowest price of this date Volume: An empty column Change %: Compared to the previous date . . . Please modify the codes from Lecture 11 and report the results of Mean Absolute Error and Root Mean Square Error on both Training dataset and Test dataset. Please note You want to predict the price after 30 days with the previous prices of 60 days. (Note it is different from the prediction setup we practiced in Lecture 11. The difference is illustrated in the figure below.) You use previous 60 day prices to build model Quiz 2: 60 day ago Today 30 day later Lecture 11: 60 day ago Price that you predict Today Tomorrow You use previous 60 day prices to build model . 0 You split the dataset with the ratio 7-to-3 for the training set and the test set in chronological order. You use 100 LSTM nodes in the layer. You optimize the model for 50 epochs. You make a plot of the Mean Absolute Error vs. Epochs for training and test data. You make a plot for comparing the real S&P 500 index and the predicted price from your model. You need to explain what can be the reason that you get inferior prediction result for 30 days later compared to the prediction for tomorrow. . You may add more layers in your LSTM model to achieve better results after reporting the required tasks. Purchase answer to see full attachment