For time-series analysis, it is possible to develop a linear regression model that simply fits a line to the variable's historical performance and extrapolates that into the future.

Learn how ARIMA models use time series data for accurate short-term forecasting. Discover its pros, cons, and essential tips for financial predictions.

Many forecasting or prediction problems involve time series data. That makes XGBoost an excellent companion for InfluxDB, the open source time series database.

Learn how to do time series regression using a neural network, with 'rolling window' data, coded from scratch, using Python.

The benefits of regression analysis are manifold: The regression method of forecasting is used for, as the name implies, forecasting and finding the causal relationship between variables.

For vector time series we compare the forecast efficiencies of two alternative approaches: first model and forecast and then form the linear combination, and first form the linear combination and then ...

In this paper, a logit model is developed using 1985 American Housing Survey (AHS) data to predict the probability of homeownership for a given household. The variables used as regressors describe ...

IBM is bringing the power of conditional reasoning to its open source Granite 3.2 LLM, in an effort to solve real enterprise AI challenges.