Observing Trends and Making Predictions with Time Series Analysis

Time series data, also known as time-stamped data, is a sequence of data points indexed in time order. These "time stamps" are data collected at different points in time. From tracking daily, hourly or weekly data to tracking logs or changes in application performance - Time series data can be found in many fields, including financial, business, weather, medicine, and more. 

This webinar discusses the two main approaches to time series data trend analysis: Moving average and auto-regressive models. We'll examine the two main time series prediction models, the exponential smoothing and the ARIMA models. The webinar will also showcase R and Python software that one can use for time series analysis.

You Will Learn:

  • Breaking up a time series into components
  • The moving average (ML) model and the auto-regressive (AR) models for trend analysis
  • The triple exponential smoothing (Holts Winters) and ARIMA models for making predictions

[Webinar ID# 5302]

Earn 1 CEU. Credits are self-reported to the industry certifying bodies. Check their respective websites for details/qualifications.

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Chris Mawata

Chris has over 30 years of IT experience, including 17 years of teaching at the university level, and 15 years of training Java and Big Data programmers. As a Learning Tree instructor, Chris has authored four courses. As a consultant, he runs a 20-node cluster on which he has several Big Data frameworks installed. He has published peer-reviewed papers in image processing, artificial intelligence, and pure mathematics.

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