- #1
wess80
- 1
- 0
Hi,
I am currently investigating environmental time series and algorithms to determine when an 'unexpected' event/reading has occurred in the series. I am currently constructing the gaussian probability density function (pdf) based on historical readings and checking if 'new' readings are acceptable according to a threshold in the pdf. As time goes by the pdf threshold becomes too large. Also, my time series contains daily and seasonal components.
I have received advice to use spectral analysis (using an FFT) on new month's data to compute the new month's periodicities. I have done so and now am faced with the question of how and what to do with the periodic frequencies in order to help determine what/when an unexpected reading is (according to the distribution).
I was thinking that perhaps it is possible to update a time series' pdf according to it's Fourier transform?
Could some one guide on this.
Thanks,
Wess
I am currently investigating environmental time series and algorithms to determine when an 'unexpected' event/reading has occurred in the series. I am currently constructing the gaussian probability density function (pdf) based on historical readings and checking if 'new' readings are acceptable according to a threshold in the pdf. As time goes by the pdf threshold becomes too large. Also, my time series contains daily and seasonal components.
I have received advice to use spectral analysis (using an FFT) on new month's data to compute the new month's periodicities. I have done so and now am faced with the question of how and what to do with the periodic frequencies in order to help determine what/when an unexpected reading is (according to the distribution).
I was thinking that perhaps it is possible to update a time series' pdf according to it's Fourier transform?
Could some one guide on this.
Thanks,
Wess