Intel AI Academy – Time-Series Analysis

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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