By Norbert Wiener
Acclaimed one of many "seminal books... related in final significance to... Galileo or Malthus or Rousseau or Mill", Cybernetics was once judged by way of twenty-seven historians, economists, educators, and philosophers to be a type of books released throughout the "past 4 decades," that may have a considerable influence on public notion and motion within the years ahead." -- Saturday evaluation
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Additional info for Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine
2d1x . • Take the weighted average of the vertices in y(ti j ) to locate the vector y(ti j ) that was predicted by the vector x(ti ) y pr ed. 30) where the weighting factors ωik , are determined by the distances of the vertices in X from x(ti ), giving ωik = (|x(tik ) − x(ti )|)−1 . 31) • To calculate the mutual prediction error, take the difference of the predicted vector and the actual vector 5 Note that we have unfolded the time series into d-dimensional space. 1 Applications of Methods and Algorithms 25 β y(x) = |y pr ed (ti ) − y(ti )|.
Theor. Appl. Financ. : Nonlinear interdependence of Chinese stock markets. Quant. Financ. : Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. : Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence. Lecture notes in mathematics, vol. 898, pp. 366–381. : Spurious diemnsions from correlation algorithms applied to limited time series data. Phys. Rev. : Testing for nonlinearity in time series: the method of surrogate data. : Nonlinearity tests for time series.
Comparing with the corresponding results of the exchange rate time series, the much smaller RMSE for the CPI time series indicates that the deterministic dynamics in the CPI time series should be stronger than that in the exchange rate time series as we mentioned earlier. For all other cases, the predictions with noise reduction are even worse than the prediction without noise reduction. This means that noise reduction may have distorted the deterministic dynamics in the CPI time series, therefore, the prediction on the filtered data becomes even more difficult.