{"id":8063,"date":"2021-07-20T14:20:43","date_gmt":"2021-07-20T12:20:43","guid":{"rendered":"https:\/\/www.iese.fraunhofer.de\/blog\/?p=8063"},"modified":"2024-01-12T14:29:18","modified_gmt":"2024-01-12T13:29:18","slug":"time-series-analysis","status":"publish","type":"post","link":"https:\/\/www.iese.fraunhofer.de\/blog\/time-series-analysis\/","title":{"rendered":"Time Traveling with Data Science: Focusing on Time Series Analysis (Part 1)"},"content":{"rendered":"

Time traveling with data science: In this blog series, we will cover some of the different techniques that make up time series analysis. This first post will provide an overview of the different types of analysis possible and answer the question of what can be done with such tools. The interested reader can find more details in the authors` recently published article „A Systematic Review of Python Packages for Time Series Analysis<\/a>„.<\/p>\n

A time series is an ordered set of data points generated from successive measurements over time. For example, in the context of IoT, measurements from sensors typically comprise time series (e.g., physical values such as pressure, temperature, electrical voltage, etc.). This information can be used as a basis for analyzing the state of machines or workflows. Data scientists have a wide range of tools at their disposal to analyze time series. Some of these techniques are developed in different contexts (bioinformatics, neuroscience, signal processing, economics, etc.) and implemented in various libraries. As a consequence, it can be challenging to keep an eye on the big picture. For this reason, we decided to start our series of blog posts by giving an overview of different time series analysis tasks. This helps to first understand what can be done with time series analysis. The interested reader can also get more detailed insights into our work (Siebert et al. 2021)<\/a>, where we reviewed 40 open-source Python packages dedicated to time series data analysis.<\/p>\n

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More about data science: What does Paul Bocuse have to do with data science?<\/a><\/p>\n<\/div>\n

The literature of time series analysis \u2013 also called time series data mining \u2013 has defined several tasks (see, for instance, (Esling and Agon 2012)). Below, we provide a selective overview.<\/p>\n

Indexing<\/strong><\/h3>\n

One of the fundamental tasks is called Indexing<\/strong> (also called query by content). Indexing is the task of finding similar time series (or a given pattern or a subsequence of a time series) in a database. Searching for similar time series is the basis for other analysis tasks (such as clustering or motif discovery, for instance).<\/p>\n