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Monthly Archives: September 2019
Time Series Trend and Seasonality Component Decomposition with STL on Spark
You may be interested in decomposing a time series into level, trend, seasonality and remainder components to gain more insight into your time series. You may also be interested in decomposition to separate out the remainder component for anomaly detection. … Continue reading
Posted in Anomaly Detection, Big Data, Data Science, ETL, Spark, Time Series Analytic
Tagged seasonal cycle, STL, time series decomposition, trend
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