Hilbert spectral analysis
Hilbert Spectral Analysis is a fancy way of looking at signals (like sound waves or stock prices) and trying to understand how they change over time.
Imagine you are drawing a picture that starts as a straight line, but then gets wavy and curly as you move across the paper. Hilbert spectral analysis helps us understand how those waves change over time, and how they relate to the original straight line.
To do this, we use something called the Hilbert transform, which is a mathematical tool that takes a signal and separates it into two parts: the part that happens very quickly (like the wavy parts of the picture), and the part that happens slowly (like the straight line part).
Once we have these two parts, we can use them to create what's called a spectrogram. This is a picture that shows how different parts of the signal change over time, with the wavy parts being shown in different colors depending on how fast they happen.
So, in summary, Hilbert spectral analysis is a tool to help us understand how signals change over time by separating them into fast and slow parts, and then creating pictures that show us how those fast parts are changing.
Imagine you are drawing a picture that starts as a straight line, but then gets wavy and curly as you move across the paper. Hilbert spectral analysis helps us understand how those waves change over time, and how they relate to the original straight line.
To do this, we use something called the Hilbert transform, which is a mathematical tool that takes a signal and separates it into two parts: the part that happens very quickly (like the wavy parts of the picture), and the part that happens slowly (like the straight line part).
Once we have these two parts, we can use them to create what's called a spectrogram. This is a picture that shows how different parts of the signal change over time, with the wavy parts being shown in different colors depending on how fast they happen.
So, in summary, Hilbert spectral analysis is a tool to help us understand how signals change over time by separating them into fast and slow parts, and then creating pictures that show us how those fast parts are changing.