Singular spectral analysis of periodic series with period L

In summary, the conversation discusses a time series with a period of L and a trajectory matrix with a window length of L. The second column of the matrix will have the same entry as the first column, resulting in a reduced rank of 1 when performing SVD due to the constant values of the time series. The conversation also raises the question of what value of L should be used to ensure the non-reduction of the matrix into one rank.
  • #1
Adel Makram
635
15
Let's have a time series with a period=L. Suppose we arbitrarily choose the window length of the trajectory matrix to be equal to L which is also equal to the period of a time series. Then the second column of the matrix will also start with the same entry as the first column, because all columns are of length L which is also equal to the period. But if we perform SVD of the matrix, we should get a reduced rank of 1, because all columns are alike. So what is the interpretation of that case?
 
  • #3
Ok, here is an attached image of the tarjectory matrix X, the column vector of length L which is the window length of the series. Now suppose that the time series that is represented by this matrix has a period which is just equal to the time between 2 successful Xs values. For example, the period of the time series is equal to the time between x1 and x2 which is also equal to the time between X2 and X3 and so forth ( sorry I mentioned, the period =L in the origial post). In other words, the time series has a constant value as a function of time if we only scan it with time intervals =the time difference between 2 successful Xs. Now the matrix surely degenerates into a rank one matrix on doing Singular Value Decomposition (SVD) operation. Then what is the interpretation of that case? And in general, what value of L should be used to grantee the non-reduction of the matrix into one rank?
 

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Related to Singular spectral analysis of periodic series with period L

1. What is singular spectral analysis (SSA)?

Singular spectral analysis is a data analysis technique used to decompose a time series into its individual components, such as trends, seasonal patterns, and noise. It is based on the principle of singular value decomposition, which separates a matrix of data into its underlying components.

2. How does SSA differ from other time series analysis methods?

Unlike other methods such as Fourier analysis, SSA does not require the time series to be periodic or have a fixed number of data points. It can handle irregularly sampled data and is able to extract both linear and nonlinear components from a time series.

3. Can SSA be used for any type of time series?

SSA is best suited for time series that exhibit some degree of periodicity or have a mix of trends and seasonal patterns. However, it may not be as effective for highly irregular or noisy time series.

4. What is the significance of the period L in SSA?

The period L in SSA represents the length of the data window used for the analysis. It determines the maximum frequency that can be resolved and affects the accuracy of the decomposition. Choosing an appropriate L is important for obtaining meaningful results from SSA.

5. How can SSA be applied in practical scientific research?

SSA has been successfully applied in various fields such as climatology, meteorology, finance, and geophysics. It can help identify patterns and trends in time series data, make predictions, and detect anomalies. It is a valuable tool for understanding and analyzing complex systems.

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