boxjenkins.dat

一个用MATLAB编写的优化控制工具箱

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% This file obtained from the web site (on 3/29/99)
% 
% http://neural.cs.nthu.edu.tw/jang/benchmark/
% 
% that is the 
% IEEE Neural Networks Council Standards Committee
% Working Group on Data Modeling Benchmarks
%
% Comment from that web site:
%
% The goal of the Working Group on Data Modeling Benchmarks (under IEEE Neural Networks Council 
% Standards Committee) is to provide an easy access to references to modeling 
% approaches and related datasets via WWW. Hopefully this can facilitate further 
%  research and comparisons on computational data modeling approaches, including artificial 
% neural networks, fuzzy inference systems, CART, MARS, and all nonlinear regression 
% and optimization techniques. 
%
% This page is constantly under construction, but the lists of datasets/publications 
% are by no means complete. If you have new datasets/papers and would like to
% put them here, please send me an email ([e-mail]) to let me know where 
% to link them. Any feedbacks and suggestions are also highly welcome. 
%
% J.-S. Roger Jang
% CS Dept., Tsing Hua Univ., Taiwan 
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%
% The file obtained is below (without the lines commented out)
%
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%----------------------------------------
%"Box and Jenkins furnace data from:
% G.E.P. Box and G.M. Jenkins
% Time Series Analysis, Forecasting and Control
% San Francisco, Holden Day, 1970,  pp. 532-533.
%
% There are originally 296 data points {y(t),u(t)}, from t=1 to t=296.
% y(t) is the output CO2 concentration and u(t) is the input gas flow
% rate.  Here we are trying to predict y(t) based on {y(t-1), y(t-2),
% y(t-3), y(t-4), u(t-1), u(t-2), u(t-3), u(t-4), u(t-5), u(t-6)}.  This
% reduces the number of effective data points to 290.
% Most methods find that the best set of input variables for predicting
% y(t) is {y(t-1),u(t-4)}.  Sugeno and Yasukawa has found that the best
% set of input variables for predicting y(t) is {y(t-1), u(t-4), u(t-3)}.
%
% The first column below is the output variable y(t), the remaining
% columns are the input variables {y(t-1), y(t-2),...u(t-6)}.
%"
%
%output y(t);
%input y(t-1) y(t-2) y(t-3) y(t-4) u(t-1) u(t-2) u(t-3) u(t-4) u(t-5) u(t-6) ;
   5.27e+01   5.31e+01   5.34e+01   5.35e+01   5.35e+01   4.41e-01   3.73e-01   3.39e-01   1.78e-01   0.00e+00  -1.09e-01
   5.24e+01   5.27e+01   5.31e+01   5.34e+01   5.35e+01   4.61e-01   4.41e-01   3.73e-01   3.39e-01   1.78e-01   0.00e+00
   5.22e+01   5.24e+01   5.27e+01   5.31e+01   5.34e+01   3.48e-01   4.61e-01   4.41e-01   3.73e-01   3.39e-01   1.78e-01
   5.20e+01   5.22e+01   5.24e+01   5.27e+01   5.31e+01   1.27e-01   3.48e-01   4.61e-01   4.41e-01   3.73e-01   3.39e-01
   5.20e+01   5.20e+01   5.22e+01   5.24e+01   5.27e+01  -1.80e-01   1.27e-01   3.48e-01   4.61e-01   4.41e-01   3.73e-01
   5.24e+01   5.20e+01   5.20e+01   5.22e+01   5.24e+01  -5.88e-01  -1.80e-01   1.27e-01   3.48e-01   4.61e-01   4.41e-01
   5.30e+01   5.24e+01   5.20e+01   5.20e+01   5.22e+01  -1.055e+00  -5.88e-01  -1.80e-01   1.27e-01   3.48e-01   4.61e-01
   5.40e+01   5.30e+01   5.24e+01   5.20e+01   5.20e+01  -1.421e+00  -1.055e+00  -5.88e-01  -1.80e-01   1.27e-01   3.48e-01
   5.49e+01   5.40e+01   5.30e+01   5.24e+01   5.20e+01  -1.52e+00  -1.421e+00  -1.055e+00  -5.88e-01  -1.80e-01   1.27e-01
   5.60e+01   5.49e+01   5.40e+01   5.30e+01   5.24e+01  -1.302e+00  -1.52e+00  -1.421e+00  -1.055e+00  -5.88e-01  -1.80e-01
   5.68e+01   5.60e+01   5.49e+01   5.40e+01   5.30e+01  -8.14e-01  -1.302e+00  -1.52e+00  -1.421e+00  -1.055e+00  -5.88e-01
   5.68e+01   5.68e+01   5.60e+01   5.49e+01   5.40e+01  -4.75e-01  -8.14e-01  -1.302e+00  -1.52e+00  -1.421e+00  -1.055e+00
   5.64e+01   5.68e+01   5.68e+01   5.60e+01   5.49e+01  -1.93e-01  -4.75e-01  -8.14e-01  -1.302e+00  -1.52e+00  -1.421e+00
   5.57e+01   5.64e+01   5.68e+01   5.68e+01   5.60e+01   8.80e-02  -1.93e-01  -4.75e-01  -8.14e-01  -1.302e+00  -1.52e+00
   5.50e+01   5.57e+01   5.64e+01   5.68e+01   5.68e+01   4.35e-01   8.80e-02  -1.93e-01  -4.75e-01  -8.14e-01  -1.302e+00
   5.43e+01   5.50e+01   5.57e+01   5.64e+01   5.68e+01   7.71e-01   4.35e-01   8.80e-02  -1.93e-01  -4.75e-01  -8.14e-01
   5.32e+01   5.43e+01   5.50e+01   5.57e+01   5.64e+01   8.66e-01   7.71e-01   4.35e-01   8.80e-02  -1.93e-01  -4.75e-01
   5.23e+01   5.32e+01   5.43e+01   5.50e+01   5.57e+01   8.75e-01   8.66e-01   7.71e-01   4.35e-01   8.80e-02  -1.93e-01
   5.16e+01   5.23e+01   5.32e+01   5.43e+01   5.50e+01   8.91e-01   8.75e-01   8.66e-01   7.71e-01   4.35e-01   8.80e-02
   5.12e+01   5.16e+01   5.23e+01   5.32e+01   5.43e+01   9.87e-01   8.91e-01   8.75e-01   8.66e-01   7.71e-01   4.35e-01
   5.08e+01   5.12e+01   5.16e+01   5.23e+01   5.32e+01   1.263e+00   9.87e-01   8.91e-01   8.75e-01   8.66e-01   7.71e-01
   5.05e+01   5.08e+01   5.12e+01   5.16e+01   5.23e+01   1.775e+00   1.263e+00   9.87e-01   8.91e-01   8.75e-01   8.66e-01
   5.00e+01   5.05e+01   5.08e+01   5.12e+01   5.16e+01   1.976e+00   1.775e+00   1.263e+00   9.87e-01   8.91e-01   8.75e-01
   4.92e+01   5.00e+01   5.05e+01   5.08e+01   5.12e+01   1.934e+00   1.976e+00   1.775e+00   1.263e+00   9.87e-01   8.91e-01
   4.84e+01   4.92e+01   5.00e+01   5.05e+01   5.08e+01   1.866e+00   1.934e+00   1.976e+00   1.775e+00   1.263e+00   9.87e-01
   4.79e+01   4.84e+01   4.92e+01   5.00e+01   5.05e+01   1.832e+00   1.866e+00   1.934e+00   1.976e+00   1.775e+00   1.263e+00
   4.76e+01   4.79e+01   4.84e+01   4.92e+01   5.00e+01   1.767e+00   1.832e+00   1.866e+00   1.934e+00   1.976e+00   1.775e+00
   4.75e+01   4.76e+01   4.79e+01   4.84e+01   4.92e+01   1.608e+00   1.767e+00   1.832e+00   1.866e+00   1.934e+00   1.976e+00
   4.75e+01   4.75e+01   4.76e+01   4.79e+01   4.84e+01   1.265e+00   1.608e+00   1.767e+00   1.832e+00   1.866e+00   1.934e+00
   4.76e+01   4.75e+01   4.75e+01   4.76e+01   4.79e+01   7.90e-01   1.265e+00   1.608e+00   1.767e+00   1.832e+00   1.866e+00
   4.81e+01   4.76e+01   4.75e+01   4.75e+01   4.76e+01   3.60e-01   7.90e-01   1.265e+00   1.608e+00   1.767e+00   1.832e+00
   4.90e+01   4.81e+01   4.76e+01   4.75e+01   4.75e+01   1.15e-01   3.60e-01   7.90e-01   1.265e+00   1.608e+00   1.767e+00
   5.00e+01   4.90e+01   4.81e+01   4.76e+01   4.75e+01   8.80e-02   1.15e-01   3.60e-01   7.90e-01   1.265e+00   1.608e+00
   5.11e+01   5.00e+01   4.90e+01   4.81e+01   4.76e+01   3.31e-01   8.80e-02   1.15e-01   3.60e-01   7.90e-01   1.265e+00
   5.18e+01   5.11e+01   5.00e+01   4.90e+01   4.81e+01   6.45e-01   3.31e-01   8.80e-02   1.15e-01   3.60e-01   7.90e-01
   5.19e+01   5.18e+01   5.11e+01   5.00e+01   4.90e+01   9.60e-01   6.45e-01   3.31e-01   8.80e-02   1.15e-01   3.60e-01
   5.17e+01   5.19e+01   5.18e+01   5.11e+01   5.00e+01   1.409e+00   9.60e-01   6.45e-01   3.31e-01   8.80e-02   1.15e-01
   5.12e+01   5.17e+01   5.19e+01   5.18e+01   5.11e+01   2.67e+00   1.409e+00   9.60e-01   6.45e-01   3.31e-01   8.80e-02
   5.00e+01   5.12e+01   5.17e+01   5.19e+01   5.18e+01   2.834e+00   2.67e+00   1.409e+00   9.60e-01   6.45e-01   3.31e-01
   4.83e+01   5.00e+01   5.12e+01   5.17e+01   5.19e+01   2.812e+00   2.834e+00   2.67e+00   1.409e+00   9.60e-01   6.45e-01
   4.70e+01   4.83e+01   5.00e+01   5.12e+01   5.17e+01   2.483e+00   2.812e+00   2.834e+00   2.67e+00   1.409e+00   9.60e-01
   4.58e+01   4.70e+01   4.83e+01   5.00e+01   5.12e+01   1.929e+00   2.483e+00   2.812e+00   2.834e+00   2.67e+00   1.409e+00
   4.56e+01   4.58e+01   4.70e+01   4.83e+01   5.00e+01   1.485e+00   1.929e+00   2.483e+00   2.812e+00   2.834e+00   2.67e+00
   4.60e+01   4.56e+01   4.58e+01   4.70e+01   4.83e+01   1.214e+00   1.485e+00   1.929e+00   2.483e+00   2.812e+00   2.834e+00
   4.69e+01   4.60e+01   4.56e+01   4.58e+01   4.70e+01   1.239e+00   1.214e+00   1.485e+00   1.929e+00   2.483e+00   2.812e+00
   4.78e+01   4.69e+01   4.60e+01   4.56e+01   4.58e+01   1.608e+00   1.239e+00   1.214e+00   1.485e+00   1.929e+00   2.483e+00
   4.82e+01   4.78e+01   4.69e+01   4.60e+01   4.56e+01   1.905e+00   1.608e+00   1.239e+00   1.214e+00   1.485e+00   1.929e+00
   4.83e+01   4.82e+01   4.78e+01   4.69e+01   4.60e+01   2.023e+00   1.905e+00   1.608e+00   1.239e+00   1.214e+00   1.485e+00
   4.79e+01   4.83e+01   4.82e+01   4.78e+01   4.69e+01   1.815e+00   2.023e+00   1.905e+00   1.608e+00   1.239e+00   1.214e+00
   4.72e+01   4.79e+01   4.83e+01   4.82e+01   4.78e+01   5.35e-01   1.815e+00   2.023e+00   1.905e+00   1.608e+00   1.239e+00
   4.72e+01   4.72e+01   4.79e+01   4.83e+01   4.82e+01   1.22e-01   5.35e-01   1.815e+00   2.023e+00   1.905e+00   1.608e+00
   4.81e+01   4.72e+01   4.72e+01   4.79e+01   4.83e+01   9.00e-03   1.22e-01   5.35e-01   1.815e+00   2.023e+00   1.905e+00
   4.94e+01   4.81e+01   4.72e+01   4.72e+01   4.79e+01   1.64e-01   9.00e-03   1.22e-01   5.35e-01   1.815e+00   2.023e+00
   5.06e+01   4.94e+01   4.81e+01   4.72e+01   4.72e+01   6.71e-01   1.64e-01   9.00e-03   1.22e-01   5.35e-01   1.815e+00
   5.15e+01   5.06e+01   4.94e+01   4.81e+01   4.72e+01   1.019e+00   6.71e-01   1.64e-01   9.00e-03   1.22e-01   5.35e-01
   5.16e+01   5.15e+01   5.06e+01   4.94e+01   4.81e+01   1.146e+00   1.019e+00   6.71e-01   1.64e-01   9.00e-03   1.22e-01
   5.12e+01   5.16e+01   5.15e+01   5.06e+01   4.94e+01   1.155e+00   1.146e+00   1.019e+00   6.71e-01   1.64e-01   9.00e-03
   5.05e+01   5.12e+01   5.16e+01   5.15e+01   5.06e+01   1.112e+00   1.155e+00   1.146e+00   1.019e+00   6.71e-01   1.64e-01
   5.01e+01   5.05e+01   5.12e+01   5.16e+01   5.15e+01   1.121e+00   1.112e+00   1.155e+00   1.146e+00   1.019e+00   6.71e-01
   4.98e+01   5.01e+01   5.05e+01   5.12e+01   5.16e+01   1.223e+00   1.121e+00   1.112e+00   1.155e+00   1.146e+00   1.019e+00
   4.96e+01   4.98e+01   5.01e+01   5.05e+01   5.12e+01   1.257e+00   1.223e+00   1.121e+00   1.112e+00   1.155e+00   1.146e+00
   4.94e+01   4.96e+01   4.98e+01   5.01e+01   5.05e+01   1.157e+00   1.257e+00   1.223e+00   1.121e+00   1.112e+00   1.155e+00
   4.93e+01   4.94e+01   4.96e+01   4.98e+01   5.01e+01   9.13e-01   1.157e+00   1.257e+00   1.223e+00   1.121e+00   1.112e+00