|
I have a sample set of values that were taken over a period of time. However, the delta time between each sample is different. Do I need to account for the different time deltas in the std-dev? Is std-dev even appropriate for this kind of data? More info... The data are temperature samples. |
|||||||||||
|
|
Yes, you do need to account for the irregularity of the time series because volatility scales with time. Depending upon the distribution and independence assumptions, sometimes a "square root of time" rule can be appropriate. Is this data sampled irregularly intraday or across a longer time period? What kind of data is it? For dealing with high-frequency financial data, you can apply a realized volatility measure, which is available in R in the realized package. |
|||
|
|
|
Based on Shane's answer I entered some data into Excel. What I came up with is that I need to multiple the square of the different from the mean by the number of seconds until the next sample. This assumes the reading was steady just until the next reading. |
|||
|
|
