Spikes can be taken as absolute quantities of measuring values which are large than approximately four (expressed as variable [Times_SD] in the program)times of the standard deviation of the time series, and can be removed by repeating 3 times with each time series. When a measuring value with the deviation from the mean larger than four times of the standard deviation, the variable can be taken as NO_VALUE, and the number of spikes is saved into the variable [SpikeNum].
If the variable [Times_SD] is taken as four, many records will be removed, so the variable [Times_SD] can be taken as larger, for example eight.
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length.
It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
This a GUI that manages DSP analysis functions for wav-files (e.g., speech signals). Two functions (plotps.m and spect.m) are included for starters. You may write your own functions and integrate that into the GUI without much hassle (see instructions in the accompanying readme.txt file). Additional features like the snipper lets you trim the time series and save it as a separate wav-file. The GUI is a great tool for instructors in a DSP course and DSP researchers alike!
Features a unique program to estimate the power spectral density. The spectrum containing all significant details is calculated from a time series model. Model type as well as model order are determined automatically from the data, using statistical criteria. Robust estimation algorithms and order selection criteria are used to obtain reliable results. Unlike in FFT analysis, where the experimenter has to set the amount of smoothing of the raw FFT, the right level of detail is assessed using the data only.
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio Optimization Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
