Time series data and the value of TSDB

Time-series data, also known as time-stamped data, is data that is sequentially observed over time and indexed by time. Time series data is everywhere around us. All events are in time, so we are always in contact with a wide variety of time series data.

Time series data is used to track everything from weather, fertility, disease incidence, heart rate, market indicators to server, application, and network performance. Analysis of time series data plays an important role in various fields such as meteorology, geology, finance, social science, physical science, epidemiology, and manufacturing. Monitoring, prediction and anomaly detection are some of its main use cases.

Why is time series data important?

The value of time series data lies in the insights that can be extracted from tracking and analysis. Understanding how a particular data point changes over time is the basis of many statistical and business analyses. Being able to track how stock prices have changed over time allows us to make more informed guesses about the future performance of stock prices at the same intervals. Analyzing time series data can lead to better decision making, new revenue models, and faster business innovation. Read the following articles to find out how different industries are leveraging time series for their use cases. Examples of these time-series case studies..

Example of time series data

Time-series data is not only the measured values ​​that occur in the time series, but also the measured values ​​that increase when added with time as the axis. To determine if your dataset is time series, check if one of the axes is time. For example, you can use time-series data to track changes in room space temperature, CPU usage of some software, or stock prices over time.

Time series data can be divided into two categories: regular time series data and irregular time series data: metrics and events. Here are some examples.

  • Periodic Time Series Data (Metrics): Daily stock prices, quarterly profits, annual sales, weather data, river flow, barometric pressure, heart rate, and pollution data are all examples of regular time series data. Periodic time series data is collected on a regular basis and is called a metric.
  • Irregular Time Series Data (Events): Time series data can occur at irregular time intervals and is called an event. Examples include log and trace, ATM withdrawals, account deposits, seismic activity, login or account registration, content consumption, and manufacturing or production process data such as processing time, inspection time, travel time, and latency.

Time series data can show high particle size at a frequency of microseconds or nanoseconds.

Copyright © 2021 IDG Communications Co., Ltd.

Time series data and the value of TSDB

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