Definition
Time-series data is a sequence of measured values, each tied to a specific timestamp (a temperature reading every second, a vibration level every minute) used to observe how a quantity changes over time. It is the specific data type a data historian is optimized to store and retrieve, and the primary input for condition-monitoring trend analysis.
Key facts
- Defined by the timestamp-value pairing structure, regardless of what is being measured.
- The type of data a data historian is specifically optimized to store and query efficiently at scale.
- The primary raw input for condition-monitoring trend analysis and many predictive-analytics applications.
- Consistent, accurate timestamps are essential: gaps or clock drift can distort trend interpretation.
How does Time-series data work in maintenance?
Time-series data consists of a sequence of values, each associated with a specific point in time, allowing a quantity (temperature, vibration, pressure, current) to be tracked as it changes, revealing trends, cycles, or sudden shifts that a single point-in-time reading cannot show.
What does Time-series data require?
A data historian is the specific type of database optimized to ingest, store, and efficiently retrieve large volumes of time-series data at high frequency over long periods. Time-series data is the content; the data historian is the storage system built specifically to handle that content well.
FAQ
Frequently asked questions
What characterizes a sequence of time-ordered measurements?
Time-series data is characterized by measured values each tied to a specific timestamp, used to observe how a quantity changes over time, and is the data type a data historian is specifically built to store efficiently.
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