Time series data is a sequence of data points collected or recorded at successive points in time, typically at uniform intervals. Its key characteristic is the temporal ordering of observations, which makes it essential for analyzing trends, patterns, and changes over time.
Related:
- Time Series Forecasting
- Anomaly Detection in Time Series
- Querying Time Series
- Joining Time Series
- Time Series Python Packages
To investigate:
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.
Relevant Resources
- In ML_Tools, see the TimeSeries folder: GitHub: Time Series Explorations