Intel AI Academy – Time-Series Analysis

This course teaches about time-series analysis and the methods used to predict, process, and recognize sequential data. Topics include:

• An introduction to time series and stationary data
• Applications such as data smoothing, autocorrelation, and AutoRegressive Integrated Moving Average (ARIMA) models
• Advanced time-series concepts such as Kalman filters and Fourier transformations
• Deep learning architectures and methods used for time series analysis

By the end of this course, students will have practical knowledge of:

• Time-series analysis theory and methods
• Key concepts that include filters, signal transformations, and anomalies
• How to use deep learning, autocorrelation, and ARIMA with Python*

The course is structured around eight weeks of lectures and exercises. Each week requires three hours to complete.

Prior Knowledge

• Python programming
• Working knowledge of pandas and scikit-learn
• Basic statistics