You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: SY_DynWin.m
    path of file: ./repos/hctsa/Operations
    
    the code of the file until where you have to start completion: function out = SY_DynWin(y,maxNumSegments)
% SY_DynWin  How stationarity estimates depend on the number of time-series subsegments
%
% Specifically, variation in a range of local measures are implemented: mean,
% standard deviation, skewness, kurtosis, ApEn(1,0.2), SampEn(1,0.2), AC(1),
% AC(2), and the first zero-crossing of the autocorrelation function.
%
% The standard deviation of local estimates of these quantities across the time
% series are calculated as an estimate of the stationarity in this quantity as a
% function of the number of splits, n_{seg}, of the time series.
%
%---INPUTS:
%
% y, the input time series.
%
% maxNumSegments, the maximum number of segments to consider. Sweeps from 2 to
%                   maxNumSegments.
%
%
%---OUTPUTS:
%
% The standard deviation of this set of 'stationarity' estimates
% across these window sizes.

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3
    
function of the number of splits, n_{seg}, of the time series.
%
%---INPUTS:
%
% y, the input time series.
%
% maxNumSegments, the maximum number of segments to consider. Sweeps from 2 to
%                   maxNumSegments.
%
%
%---OUTPUTS:
%
% The standard deviation of this set of 'stationarity' estimates
% across these window sizes.

% ------------------------------------------------------------------------------
% Copyright (C) 2020, Ben D. Fulcher <ben.d.fulcher@gmail.com>,
% <http://www.benfulcher.com>
%
% If you use this code for your research, please cite the following two papers:
%
% (1) B.D. Fulcher and N.S. Jones, "hctsa: A Computational Framework for Automated
% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).
% DOI: 10.1016/j.cels.2017.10.001
%
% (2) B.D. Fulcher, M.A. Little, N.S. Jones, "Highly comparative time-series
% analysis: the empirical structure of time series and their methods",
% J. Roy. Soc. Interface 10(83) 20130048 (2013).
% DOI: 10.1098/rsif.2013.0048
%
% This function is free software: you can redistribute it and/or modify it under
% the terms of the GNU General Public License as published by the Free Software
% Foundation, either version 3 of the License, or (at your option) any later
% version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT
% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
% details.
%
% You should have received a copy of the GNU General Public License along with
% this program. If not, see <http://www.gnu.org/licenses/>.
% ------------------------------------------------------------------------------

if nargin < 2 || isempty(maxNumSegments)
    maxNumSegments = 10;
end

nsegr = (2:1:maxNumSegments); % range of nseg to sweep across
nmov = 1; % controls window overlap

numFeatures = 11; % number of features
fs = zeros(length(nsegr),numFeatures); % standard deviation of feature values over windows
taug = CO_FirstCrossing(y,'ac',0,'discrete'); % global tau

for i = 1:length(nsegr)
    nseg = nsegr(i);
    wlen = floor(length(y)/nseg); % window length
    inc = floor(wlen/nmov); % increment to move at each step
    % If increment rounded down to zero, prop it up
    if inc == 0
        inc = 1;
    end