Time series data is data recorded as a sequence of observations over time, where the order and spacing of the points carry meaning. Each record pairs a timestamp with one or more values: hourly energy consumption, daily transaction totals, or sensor readings sampled every few seconds.
Because the sequence itself is information, time series data behaves differently from tabular snapshots. Models trained on it depend on trends, seasonality, and gaps, so shuffled rows or missing intervals can quietly break results. Common tasks include forecasting, anomaly detection, and pattern mining.
In enterprise AI, time series data is one of the three data types teams most often need to make AI-ready, alongside tabular and text data. Preparing it usually means repairing missing intervals, aligning sampling frequencies, and recording exactly which window of history each model run used.
Related terms: Tabular Data · Data Drift · Anomaly Detection · Data Reproducibility