You train a number of algorithms/models on time series in the training data, observe which algorithm performs the best on the validation data and choose that one. You typically divide the time series into three groups, the training data, the validation data and the test data. Supervised problems have the following procedure: You get a set of time series, each with a class label. In essence, time series classification is a type of supervised machine learning problem. What is time series classification with machine learning? You can imagine yourself that the applications of good algorithms are essentially without limit. Classification of brain imaging or genetic expression data.Internet-of-things: classify whether a kitchen device is malfunctioning.Surveillance: From a video, capture the path of an individual, then classify what he/she is doing.Classify an ECG as normal or give the type of abnormality.Time series classification problems are everywhere, so it is hard to know where to start, but the following are some random examples of making classifications from time series data: The beauty of this is that it lends the possibility to analyze time series using language models and to analyze language using time series models. Other types of data can also be viewed as/transformed into time series, such as written text, which is basically a time series but where the entities are not numeric. The growth curve of a child ( here the time points are not equally spaced, so this puts special demands on the algorithms).Internet-of-things and other sensor data.Note that here the data is also ordered in the pixel dimensions). Video ( a multi-dimensional time series where each image corresponds to a time point.The temperature in Stockholm each day during 2020 ( a uni-dimensional time series).They are ubiquitous since anything numeric that you measure over time or in a sequence is a time series. Examples of time series and classification problemsĪ time series is just one (uni-dimensional) or several (multi-dimensional) temporally ordered sequences of numeric values. Adapted from with permission from Patrick Schäfer. Each time series belongs to one of three classes: cylinders, bells and funnels. Samples from the widely used synthetic Cylinder-Bell-Funnel (CBF) benchmark dataset.
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