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.
Get ready to level up your time-series forecasting game! In this tutorial, we’re going to take things up a notch by showing you how to adjust prediction intervals using Keras recurrent neural networks and Python. Now, you may remember our previous article on stock market forecasting where we made a forecast for the S&P500 stock … Read more
