Time series regression is a type of regression analysis that is used to model and make predictions based on time-series data. Time series data is data that is collected and measured at regular time intervals, such as daily stock prices or monthly temperature readings. Time series regression involves fitting a regression model to time-series data, in order to identify the underlying patterns and trends in the data. This can be useful for making predictions about future values of the data, or for understanding the factors that influence the data. Time series regression can be performed using various methods, such as linear regression, exponential smoothing, and autoregressive integrated moving average (ARIMA) modeling.
This tutorial shows how to adjust prediction intervals in time series forecasting using Keras recurrent neural networks and Python. We build on a previous article on stock market forecasting, in which we created a forecast for the S&P500 stock market index. The prediction interval used in this last article was … Read more