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See Examples page on the internet from the Main Menu:
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A very low signal-to-noise dataset is analyzed to identify the presence of oscillatory signal and it's frequency.
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A multivraiate dataset is analyzed by Multi-channel SSA and Varimax Rotation to isolate spatio-otemporal oscillatory modes from red-noise.
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Climate
Using Southern Oscillation Index, we demonstrate how the quasi-periodic oscillatory modes can be tested for their significance, reconstructed and predicted by Singular Spectrum Analysis (SSA). MTM analysis, including MTM Coherence, is also demonstrated.
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Oceanography:
Multi-channel SSA is demonstrated using Global Sea Surface Temperature Anomalies dataset.
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SSA prediction of a noisy and noise-free, quasi-periodic signals. Demonstration of SSA cross-validation for choosing optimal prediction parameters.
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Hydrology:
Analysis of annual minima of Nile river demonstrates how to perform SSA detrending and SSA prediction.
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Seismology:
SSA of seismograph of the Kobe earthquake demonstrates SSA reconstruction of a bursty oscillatory signal.
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Finance:
SSA of Forex time series captures short and long term trend components.
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Paleoclimatology:
Analysis of normalized annual tree-ring data from Argentina (441-1974) shows how to customize SSA noise null-hypothesis in presence of a dominant spectral peak, and to perform separation of a low-frequency trend by SSA.
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Economics:
Analysis of monthly Australian sales of red wine (1980 - 1995) demonstrates identifying seasonal components of the spectrum, SSA detrending and prediction, and MTM reshaped spectrum.
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Health:
Analysis of a patient's test score time series. Demonstrates identification/reconstruction of trend and high-frequency oscillatory components, and compares SSA and MTM Reconstructions.
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Physics:
Analysis of a sunspot time series demonstrates how to customize various MTM settings for long time series, customize SSA noise null-hypothesis, SSA prediction and detrending.
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Analysis and prediction of a noisy multivariate time series consisting of two channels, each containing quasi-periodic oscillatory modes.
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Demonstrates novel gap-filling method for missing data. Includes analysis of a gappy and noisy time series containing quasi-periodic oscillatory modes.
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Demonstrates novel gap-filling method for missing data. Includes analysis of a gappy and noisy time series containing quasi-periodic, oscillatory spatio-temporal modes. ---------------------------------------------------------------------------------------------------------------------------------------------
Capabilities for automated data processing are demonstrated. Demo Automator workflows include processing data from multiple files and with multiple analysis tools.
See also Automator workflows in other Examples.
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AppleScript and Matlab Data I/O demos.
See Readme file in Scripts folder.
Happy fishing in the ocean of noise!
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Feel free to send your suggestions and bugs found to support@spectraworks.com.
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