About Course
This comprehensive online course on time series analysis will take you on a journey from understanding the fundamental concepts to applying advanced techniques in real-world scenarios. Whether you’re a data scientist, analyst, or researcher, this course is designed to equip you with the knowledge and skills to effectively analyze and forecast time-dependent data.
Through a combination of theoretical explanations, hands-on exercises, and practical examples using popular programming languages like R, you’ll gain the confidence to tackle complex time series problems and make data-driven decisions.
Course Content
Time Series Analysis using R
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What is Time Series
05:05 -
Time Series Analysis
03:12 -
Purpose of Time Series Analysis
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Application of Time Series Analysis
03:08 -
Important Elements of Time Series Analysis – Trend
04:41 -
Important Elements of Time Series Analysis – Seasonality
04:22 -
Important Elements of Time Series Analysis – Cyclicity
02:48 -
Important Elements of Time Series Analysis – Irregularity
01:36 -
Decomposition of Time Series Elements
03:11 -
Stationary Vs. Non-stationary
05:05 -
Why Stationary is important
05:35 -
Types of Model – AR (Autoregressive)
04:46 -
Types of Model – MA (Moving Average)
05:57 -
ACF and PACF
09:13 -
Use Case using R
12:38 -
Forecasting using R
03:03