Time Series Analysis: Understanding Seasonality and Cyclicality
SEASONALITY
- Refers to patterns that repeat over a fixed and known period, typically within a year or less.
- Patterns are often linked to natural or cultural events, such as holidays, weather patterns, or annual business cycles.Examples include higher sales of winter coats in winter and increased swimsuit sales in summer.
- Seasonal patterns exhibit regular, predictable fluctuations with consistent shape and amplitude each year.
- Methods like seasonal decomposition or seasonal ARIMA models are effective in capturing and modeling seasonality.
CYCLICALITY
- Involves patterns that repeat over an unknown or irregular period, often lasting longer than a year.
- Cycles can be influenced by economic or business cycles, technological advancements, or long-term trends.
- Unlike seasonality, cyclicality is less predictable and challenging to model due to varying cycle length and timing.
- Stock market cycles (boom and bust) lasting for years or decades exemplify cyclicality.
- Modeling cyclicality requires advanced techniques like spectral analysis or state space models.
