Basics of Time Series Key Characteristics
- Each record consists of timestamps with measurements.
- Different time series can have different frequencies (e.g., hourly, daily).
- Always check the unit of measurement for each field (e.g., cm, $, etc.).
Metric types:
- Counter: Monotonically increases.
- Gauge: Can go up or down.
- Summary: Aggregate calculation over a period.
Data Structure Considerations
- Time series data is often stored in a long column format.
- Partition datasets by period (e.g., monthly) for:
- Easier period-based querying.
- Computing aggregates.
- Focusing on recent data efficiently.
Common Applications
- Finance: Stock prices, interest rates, economic indicators.
- Weather Forecasting: Temperature, precipitation, wind speed.