A sequence of data points recorded at regular time intervals — such as hourly, daily, or monthly — used for trend analysis, pattern detection, and forecasting.
Time series data captures how a value changes over time. The time dimension is fundamental to the analysis: the order of observations matters, and patterns like seasonality, trends, and cycles are only visible when data is examined over time.
Common time series analyses include trend detection (is the value increasing or decreasing over time?), seasonality analysis (does the value follow a regular pattern within a year?), and forecasting (what will the value be in the future?).
Example: A city's open data portal publishes monthly 311 service request counts going back five years. This is a time series dataset. An analyst can plot the data to see whether total requests are trending up or down, identify seasonal patterns (more requests in summer?), and forecast expected volumes for budget planning.