Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Fixed Length Memory Model Exercise So

The video tutorial explains the concept of averages, focusing on weighted averages with parameters alpha and beta. It discusses sequence modeling in stock price prediction, emphasizing the use of running averages to incorporate historical data. The importance of hyperparameters in determining the contribution of past and current data is highlighted. The tutorial contrasts classical methods like moving averages with modern approaches such as recurrent neural networks for stock market predictions.

What role does the running average play in predicting future stock prices?

How should the contributions of historical and recent data points be adjusted according to the text?