70 lines
2.5 KiB
Mathematica
70 lines
2.5 KiB
Mathematica
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function [xt_n, Pt_n, Ptt1_n, xt_t1, Pt_t1] = kalmansmooth(y, M, maxIter, phi, mu0, sigma0, Q, sigma_sq, p, q, n)
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% Adapted from code written by Hrishi Deshpande
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% Based on eqn (A3)-(A12) of Appendix A in Shumway, R.H. & Stoffer, D.S. (1982),
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% "An approach to time series smoothing and forecasting using the EM algorithm", Journal of Time Series Analysis, 3, 253-264
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%- - - - - - - - - - - - - - - -
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% Forward steps. Eqns (A3)-(A7).
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xt_t1 = zeros(p, n+1); % t = 0, 1, ..., n
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Pt_t1 = cell (1, n+1); % t = 0, 1, ..., n
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K = cell (1, n+1); % t = 0, 1, ..., n
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xt_t = zeros(p, n+1); % t = 0, 1, ..., n
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Pt_t = cell (1, n+1); % t = 0, 1, ..., n
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t = 0; tIdx = t+1;
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xt_t(:,tIdx) = mu0;
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Pt_t{tIdx} = sigma0;
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for t = 1:n
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tIdx = t+1;
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xt_t1(:,tIdx) = phi*xt_t(:,tIdx-1); % (A3)
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Pt_t1{tIdx} = phi*Pt_t{tIdx-1}*phi' + Q; % (A4)
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if 1,
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K{tIdx} = Pt_t1{tIdx}*M{tIdx}' * inv(M{tIdx}*Pt_t1{tIdx}*M{tIdx}' + sigma_sq*eye(q)); % (A5)
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else
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% Using the Matrix Inversion Lemma
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% (see http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/zoubin/www/SALD/week5b.pdf)
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invR = eye(q)./sigma_sq; AA = inv(inv(Pt_t1{tIdx}) + M{tIdx}'*invR*M{tIdx}); BB = (invR - invR*M{tIdx}*AA*M{tIdx}'*invR);
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K{tIdx} = Pt_t1{tIdx}*M{tIdx}' * BB; % (A5)
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end
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xt_t(:,tIdx) = xt_t1(:,tIdx) + K{tIdx}*(y(:,tIdx) - M{tIdx}*xt_t1(:,tIdx)); % (A6)
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Pt_t{tIdx} = Pt_t1{tIdx} - K{tIdx}*M{tIdx}*Pt_t1{tIdx}; % (A7)
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end
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%- - - - - - - - - - - - - - - -
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% Backward steps. Eqns (A8)-(A10)
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Jt = cell (1, n+1); % t = 0, 1, ..., n
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xt_n = zeros(p, n+1); % t = 0, 1, ..., n
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Pt_n = cell (1, n+1); % t = 0, 1, ..., n
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t=n; tIdx = t+1;
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xt_n(:,tIdx) = xt_t(:,tIdx); % (A9)
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Pt_n{tIdx} = Pt_t{tIdx}; % (A10)
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for t=n:-1:1
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tIdx = t+1;
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Jt{tIdx-1} = Pt_t{tIdx-1}*phi'*inv(Pt_t1{tIdx}); % (A8)
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xt_n(:,tIdx-1) = xt_t(:,tIdx-1) + Jt{tIdx-1}*(xt_n(:,tIdx) - phi*xt_t(:,tIdx-1)); % (A9)
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Pt_n{tIdx-1} = Pt_t{tIdx-1} + Jt{tIdx-1} * (Pt_n{tIdx} - Pt_t1{tIdx}) * Jt{tIdx-1}'; % (A10)
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end
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%- - - - - - - - - - - - - - - -
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% Backward steps. Eqns (A11)-(A12)
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Ptt1_n = cell(1, n+1); % t = 0, 1, ..., n
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t = n; tIdx = t+1;
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Ptt1_n{tIdx} = (eye(p) - K{tIdx}*M{tIdx}) * phi * Pt_t{tIdx-1}; % (A12)
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for t=n:-1:2
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tIdx = t+1;
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Ptt1_n{tIdx-1} = Pt_t{tIdx-1}*Jt{tIdx-2}' + ...
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Jt{tIdx-1}*(Ptt1_n{tIdx} - phi*Pt_t{tIdx-1})*Jt{tIdx-2}'; % (A11)
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end
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