A novel real-time event prediction method for heterogeneous multivariate temporal data (time point values, instantaneous events, or time intervals) that leverages multiple instances of multiple temporal patterns

Discover, analyze and predict the temporal and spatial patterns of complex networks using Machine Learning and Deep Learning.

An exploratory data analysis of 2014 air quality in Hay Hassani. Analyzes pollutants (NO₂, O₃, PM10) & meteorology to uncover trends, exceedances, and seasonal patterns. Includes statistical summaries and visualizations.

A Pythonic implementation of the frequent patterns discovery algorithm