In this article, learn more about time series compression algorithms along with their role in real-world applications in different sectors.
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This is an article from DZone’s 2022 Database Systems Trend Report.
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Time series is present in our daily lives in multiple sectors of society, such as finance, healthcare, and energy management. Some of these domains require high data volume so that insights from analysis or forecasting the behavior of target variables can be obtained. Transferring and processing high data rates and volume across platforms with several users requires storage and computer power availability. Compression techniques are a powerful approach to avoid overwhelming systems. In what follows, time series compression algorithms will be discussed along with their role in real-world applications in different sectors.
Time series is defined as a sequence of values of a quantity obtained at successive times, often with equally spaced intervals. We experience the use of timestamped data from when we monitor our exercises with a fitness app to when we track our pizza delivery traveling through the city all the way to our doorstep. Time series is relevant to problems when understanding the evolution of a variable over time is needed, such as understanding the time profile of a variable or forecasting their values.
Time series most commonly appear in the form of timestamped numerical data in a tabular format. Audio data itself is already represented as a time series, as it is defined in terms of frequencies. However, although time series are themselves a data type, they can also be combined with other data types in order to produce more complex entities, which contain an embedded temporal aspect such as:
Time series data requires specific techniques in order to obtain insights not only from the different patterns in data, but also among those over time. In a data science pipeline, these techniques are employed during the preprocessing, analysis, and modeling steps.
