intermediate

Time series analysis — 6-topic bundle

Comprehensive AI-generated study curriculum with 1 detailed note module.

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Course Syllabus

  1. Introduction to Time Series Forecasting
  2. Simple Forecasting Methods
  3. Introduction to Exponential Smoothing
  4. Simple Exponential Smoothing (SES) Fundamentals
  5. Smoothing Parameter \(\alpha\) and its Impact
  6. Applying and Interpreting Simple Exponential Smoothing

Study Notes

Introduction to Time Series Forecasting

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  • Trend: A long-term increase or decrease in the data. Think of a growing company's sales over several years.
  • Seasonality: A pattern that repeats over a fixed period (e.g., daily, weekly, monthly, yearly). Retail sales often peak around holidays every year.
  • Cyclicality: Fluctuations that aren't fixed in period, often related to economic conditions or business cycles. These are longer than seasonal patterns.
  • Irregular (or Residual) Component: What's left over after accounting for trend, seasonality, and cycles. It's the random, unpredictable variation.
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