The value of time series data and TSDBs

Time series data, also called time-stamped data, is data that is observed sequentially over time and that is indexed by time. Time series data is all around us. Because all events exist in time, we are in constant contact with an immense variety of time series data.

Time series data is used for tracking everything from weather, birth rates, disease rates, heart rates, and market indexes to server, application, and network performance. Analysis of time series data plays an important role in disciplines as varied as meteorology, geology, finance, social sciences, physical sciences, epidemiology, and manufacturing. Monitoring, forecasting, and anomaly detection are some of its main use cases.

Why is time series data important?

The value of time series data resides in the insights that can be extracted from tracking and analyzing it. Understanding how specific data points change over time forms the foundation for many statistical and business analyses. If you can track how the stock price has changed over time, you can make a more educated guess about how it might perform over the same interval in the future. Analyzing time series data can lead to better decision making, new revenue models, and faster business innovation. To learn how various industries are putting time series to work for their use case, read some of these time series case study examples.

Time series data examples

Time series data isn’t just about measurements that happen in chronological order, but also about measurements whose value increases when you add time as an axis. To determine if your dataset is time series, check if one of your axes is time. For example, time series data can be used to track changes—over time—in the temperature of an indoor space, the CPU utilization of some software, or the price of a stock.

Time series data can be classified into two categories: regular and irregular time series data, or in other words metrics and events. Here are some examples:

  • Regular time series data (metrics): Daily stock prices, quarterly profits, annual sales, weather data, river flow rates, atmospheric pressure, heart rate, and pollution data are all examples of regular time series data. Regular time series data are collected at regular time intervals and are called metrics.
  • Irregular time series data (events): Time series data can also occur at irregular time intervals and are then called events. Examples include logs and traces, ATM withdrawals, account deposits, seismic activity, logins or account registrations, content consumption, and manufacturing or production process data like processing time, inspection time, move time, and queue time.

Time series data sometimes exhibit high granularity, as frequently as microseconds or even nanoseconds.

Copyright © 2021 IDG Communications, Inc.

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