Comments on: Detecting Seasonality Using Fourier Transforms in R https://anomaly.io/detect-seasonality-using-fourier-transform-r/ Just another WordPress site Thu, 30 May 2019 16:24:00 +0000 hourly 1 http://wordpress.org/?v=4.2.2 By: Banti Laure Mathilde Yameogohttps://anomaly.io/detect-seasonality-using-fourier-transform-r/#comment-57 Mon, 16 Jul 2018 18:52:00 +0000 https://anomaly.io/?p=2803#comment-57 Hi everyone,

My data have 24 hours periodicity but my periodogram give me these result how to interpret that? my data are sampled every 15min. Sample size: 36411

the result: freq spec 2 5.486968e-05 134.42945 1 2.743484e-05 69.86439

time= 1/top2$f
time
1 18225 36450

]]> By: Abhishek joshihttps://anomaly.io/detect-seasonality-using-fourier-transform-r/#comment-55 Wed, 06 Jun 2018 10:51:00 +0000 https://anomaly.io/?p=2803#comment-55 sir,
i was trying to study the accelerograph data , i used the periodogram function but i am having a doubt. Does the periodogram function represent all the frequencies associated with the signal. my plot shows a frequency range of 0 to 0.5 Hz. But when i am plotting the evolutivefft it shows that the frequencies are present in the range of 0 t0 50Hz

]]> By: naman bhallahttps://anomaly.io/detect-seasonality-using-fourier-transform-r/#comment-33 Wed, 23 Aug 2017 11:26:00 +0000 https://anomaly.io/?p=2803#comment-33 Coincidence. I am Naman, and I had the same problem.

]]> By: Naman Doshihttps://anomaly.io/detect-seasonality-using-fourier-transform-r/#comment-14 Mon, 14 Nov 2016 16:23:00 +0000 https://anomaly.io/?p=2803#comment-14 I tried the TSA package, and according to the frequency my supposed seasonal period exceeds the size of my dataset provided, what does this imply?

]]> By: Extract Seasonal & Trend: using decomposition in R - Anomalyhttps://anomaly.io/detect-seasonality-using-fourier-transform-r/#comment-5 Mon, 11 Jan 2016 15:10:14 +0000 https://anomaly.io/?p=2803#comment-5 […] To detect the underlying trend, we smooth the time series using the “centred moving average“. To perform the decomposition, it is vital to use a moving window of the exact size of the seasonality. Therefore, to decompose a time series we need to know it seasonality period: weekly, monthly, etc… No worry if you don’t know it, you can detect the seasonality using Fourier transform. […]

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