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How would you isolate seasonal variation in a time series?
Preliminary detection
- De-trend your data with a centered moving average the size of your estimated seasonality.
- Isolate the seasonal component with one moving average per relevant time-step (e.g. one moving average per calendar day for a weekly seasonality, or one per month for an annual seasonality).
Why is random variation problematic in a time series?
Random Walks and the Nonindependence of Observations The central point that differentiates time-series problems from most other statistical problems is that in a time series, observations are not mutually independent. In fact, cumulating nearly any series of random numbers will yield a pattern that looks nonrandom.
Is there a way to work with irregular time series?
Let’s take a quick look at working with irregular time series as well as explore some of the challenges. Join the DZone community and get the full member experience. One of the benefits of InfluxDB is the ability to store raw events, which might come in at varying intervals, as an irregular time series.
Are there any biases in an unevenly spaced time series?
However, transforming data in such a way can introduce a number of significant and hard to quantify biases, especially if the spacing of observations is highly irregular. Ideally, unevenly spaced time series are analyzed in their unaltered form.
How does InfluxDB work with irregular time series?
One of the benefits of InfluxDB is the ability to store raw events, which might come in at varying intervals, as an irregular time series. However, irregular time series present some unique challenges, and in some cases, common operations on the data simply will not work.
How to analyze an equally spaced time series?
A common approach to analyzing unevenly spaced time series is to transform the data into equally spaced observations using some form of interpolation – most often linear – and then to apply existing methods for equally spaced data.