Fourier Time Series: Breaking Down Data Into Sines & Cosines

In summary, the conversation discusses the topic of Fourier Series and its application to random time series data. The speaker is looking for ways to break down the data into sines and cosines, and is seeking guidance on how to do so. The responder confirms that it is possible to break down the data using Fourier Series and provides a helpful online demonstrator for visualization.
  • #1
I have a question regarding Fourier Series. I have a random set of time series data. Is there anyway to break this down into sines and cosines. From the literature I have read it seems like you can break down a function into sines and cosines but I need to do this for a random set of data or is this even possible(limitations of a Fourier Series). Any help on how to start on this will be greatly appreciated.
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  • #2
Yes you can do it and each spectral component (set of coefficients) will also be random.
  • #3
I found a nice online Fourier series demonstrator" [Broken]

You can select random noise and display the sine and cosine parts (unfortunately the scale factor is small), or display it in magnitude/phase with a log view.
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1. What is Fourier Time Series?

Fourier Time Series is a mathematical technique used to analyze and break down data into its component frequencies, represented by sines and cosines. It is commonly used in signal processing, physics, and engineering to study periodic or oscillatory phenomena.

2. How does Fourier Time Series work?

Fourier Time Series works by decomposing a complex signal into simpler sinusoidal components through a process called Fourier transform. This involves breaking down the signal into its constituent frequencies and amplitudes, which can then be represented as sines and cosines in a mathematical equation.

3. Why is Fourier Time Series important?

Fourier Time Series is important because it allows us to analyze and understand complex data in terms of simpler components. This can help identify patterns and trends in the data, and can also be used for data compression and noise reduction.

4. What are some applications of Fourier Time Series?

Fourier Time Series has a wide range of applications, including image and sound processing, medical imaging, climate analysis, and financial forecasting. It is also used in fields such as physics, engineering, and biology to study periodic phenomena and oscillatory systems.

5. Are there any limitations to Fourier Time Series?

While Fourier Time Series is a powerful tool for data analysis, it does have some limitations. It assumes that the data is periodic and stationary, meaning that it repeats the same pattern over time. It also does not account for nonlinear relationships between variables. Additionally, it may not work well for data with missing values or outliers.

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