# Understanding Wavelet Transform for Time Series Data Analysis

• DavidLiew
In summary, the conversation discusses the use of wavelet transform to de-noise time series data and then analyze it using a neural network. The speaker suggests using MATLAB and researching on IEEE and other sources to understand the transform better. They also mention using wavelet coefficients as input and discuss methods for determining the level of decomposition and choosing the appropriate wavelet and thresholding method. The conversation also briefly mentions the use of R-language and wavelet packets and suggests a book as a good starting point for understanding wavelets with R. Lastly, the speaker mentions the possibility of using a wavelet network for analysis.
DavidLiew
I have a time series data, I want to use wavelet transform to de-noise the data and then use neural network to analysis the transform data. However, I quite not understand about the wavelet transform. Can tell me about how to use wavelet transform? How to determine the scale? Is it suitable use the coefficients as a input to neural network for analysis?

DavidLiew said:
I have a time series data, I want to use wavelet transform to de-noise the data and then use neural network to analysis the transform data. However, I quite not understand about the wavelet transform. Can tell me about how to use wavelet transform? How to determine the scale? Is it suitable use the coefficients as a input to neural network for analysis?

MATLAB is your best bet to play around with the wavelet transform (i think they even have a toolbox)
-IEEE access and do some digging on the transforms (esp if someone used the output of them as the input to a neural network simulation) and how others have applied it..

I used the wavelet transform coefficients (daubechies) for an undergrad signal detection project...
depends on the input data (ours looked like the letter N (gunshots) with noise on it) the type and level of decomposition needed would vary (of course computational horsepower as well more levels would be slow basically u are splitting the signal by sending it thru low pass and bp filters and looking at it more closely...)
We kinda did a Normalized cross correlation (NCC) on the wavelet coefficients (incoming signal versus our database signature) and used some statistical estimation to classify when they would match (detecting the high peaks generated due to NCC)
The Wiki page is free :) (compared to IEEE and others) http://en.wikipedia.org/wiki/Daubechies_wavelet

I now using MATLAB to analysis the data with discrete wavelet transform, but I don't know how to choose the best level for decomposition and what type of wavelet to use. Have any method to determine the level and wavelet? Besides, how to choose the suitable thresholding method and noise structure?

## What is wavelet transform and how does it work?

Wavelet transform is a mathematical tool used for analyzing and processing time series data. It decomposes a signal into different frequency components, allowing for a more detailed analysis of the data. It works by breaking down the signal into small, overlapping segments and analyzing the changes in amplitude and frequency within each segment.

## Why is wavelet transform useful for time series data analysis?

Wavelet transform is useful for time series data analysis because it can capture both short-term and long-term patterns in the data. It also has the ability to handle non-stationary and irregularly sampled data, making it a versatile tool for analyzing various types of time series data.

## What are the advantages of using wavelet transform over other methods of time series analysis?

Some advantages of using wavelet transform over other methods of time series analysis include its ability to handle both time and frequency domains simultaneously, its adaptability to different types of data, and its ability to provide a more detailed analysis of the data compared to other methods.

## What are some common applications of wavelet transform in time series data analysis?

Wavelet transform has a wide range of applications in time series data analysis, including signal denoising, feature extraction, time-frequency analysis, and trend detection. It is also commonly used in fields such as finance, medicine, and geology for analyzing and predicting trends in data.

## Are there any limitations to using wavelet transform for time series data analysis?

While wavelet transform has many advantages, it also has some limitations. It can be computationally intensive and may not be suitable for analyzing very large datasets. Additionally, the interpretation of the results can be complex and may require a good understanding of wavelet theory.

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