Practical Systems Programming in Go

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Time Series Data Mining

Time series data mining is the process of discovering useful patterns, relationships, and structures within data that change over time. In practice, it involves techniques for classification, clustering, anomaly detection, forecasting, and motif or shape discovery in sequences of numeric values collected over regular intervals — such as sensor readings, stock prices, log file analytics, system metrics, service metrics or network metrics. The selection of an appropriate metric is often an essential step in all data mining tasks. This choice depends on the nature of the data and the characteristics of the patterns one is trying to capture. Different metrics may be more suitable for different applications and types of ...