stl-decomposition | 易学教程

Having a lot of issues with time series objects in R

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问题 I am having an extraordinarily difficult time dealing with -any- time series objects of some budget data. The original data is 14,460 rows of payments on ~1800 contracts, where each row has a DD/MM/YYYY and Amount feature. There are 5296 days between 1/1/2000 and 12/31/2014, but only 3133 of these days actually had payments. The days are therefore irregularly spaced, with more than one contract payment showing up on some days, and zero payments on others. The main issue I'm having is the

STL decomposition of time series with missing values for anomaly detection

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I am trying to detect anomalous values in a time series of climatic data with some missing observations. Searching the web I found many available approaches. Of those, stl decomposition seems appealing, in the sense of removing trend and seasonal components and studying the remainder. Reading STL: A Seasonal-Trend Decomposition Procedure Based on Loess , stl appears to be flexible in determining the settings for assigning variability, unaffected by outliers and possible to apply despite missing values. However, trying to apply it in R, with four years of observations and defining all the

STL decomposition of time series with missing values for anomaly detection

阅读更多关于 STL decomposition of time series with missing values for anomaly detection

问题 I am trying to detect anomalous values in a time series of climatic data with some missing observations. Searching the web I found many available approaches. Of those, stl decomposition seems appealing, in the sense of removing trend and seasonal components and studying the remainder. Reading STL: A Seasonal-Trend Decomposition Procedure Based on Loess, stl appears to be flexible in determining the settings for assigning variability, unaffected by outliers and possible to apply despite