Seasonality in time series data refers to the presence of regular and predictable patterns that repeat at fixed intervals over time. These patterns are often associated with certain periods, such as days, months, quarters, or years. Seasonality introduces fluctuations or variations in the data that are not due to underlying trends or random noise but are linked to specific time periods.
Addressing seasonality is crucial in time series analysis to better understand the data and make accurate predictions. Here’s an overview of seasonality and methods to address it:
I. Characteristics of Seasonality:
1. Regular Patterns:
- Seasonality exhibits consistent and recurring patterns at fixed intervals.
2. Temporal Consistency:
- The patterns occur in a systematic and predictable manner over time.
3. Periodic Peaks and Troughs:
- Seasonal patterns often lead to peaks and troughs in the data corresponding to specific seasons or time periods.
4. Influence on Trends:
- Seasonal effects can sometimes mask or amplify underlying trends in the data.
