Time series analysis involves identifying attributes of your time series data, such as trend and seasonality, by measuring statistical properties.

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

Visualizing time series data is often the first step in observing trends that can guide time series modeling and analysis.

This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the ...

In statistical process control, a state of statistical control is identified with a process generating independent and identically distributed random variables. It is often difficult in practice to ...

Predictive modeling uses known results to create, process, and validate a model that can be used to forecast future outcomes.

Time series data is useful for things such as predicting weather, medical events, or financial changes, and generative AI can model such sequences if given a little help, NYU scholars find.