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は、私たちは私たちがBIC基準を使用して最良のARIMAモデルを選択したコードを、次のようしていると仮定しましょう最適化コード、それはすぐに実行されるように
function [AR_order,MA_order]= complete_arima(Y)
%% if not specific input from user, use default time series
if nargin<1
Y=xlsread('ARIMA','ARIMA','d6:d468');
end
% Y is the observed time series
%% graphical representation of time series
subplot(3,1,1);
plot(Y);
title('original time series');
%%autocorrelation/partial autocorrelation of given signal
subplot(3,1,2);
autocorr(Y);
subplot(3,1,3);
parcorr(Y);
hold off
%% entering necessary parameters for Arima Testing
d=input('enter necessary order of differencing : ');
M=input('maximum order of lag for ARMA simulation : ');
%% simulate ARIMA model on the base of given input
LOGL = zeros(M,M); %Initialize
PQ = zeros(M,M);
for p = 1:M
for q = 1:M
mod = arima(p,d,q);% for each pair generate new ARIMA model
[~,~,logL] = estimate(mod,Y,'print',false);
LOGL(p,q) = logL;
PQ(p,q) = p+q;
end
end
%% calculate BIC
LOGL = reshape(LOGL,M*M,1);
PQ = reshape(PQ,M*M,1);
[~,bic] = aicbic(LOGL,PQ+1,100);
bic=reshape(bic,M,M);
%% determine order of ARIMA by finding minimum element from matrix bic and corresponding indeces
[AR_order,MA_order]=find(bic==min(min(bic)));
end
その実行時間は私がスピードアップすることができますどのように
>> tic
>> [AR_order,MA_order]=complete_arima();
enter necessary order of differencing : 1
maximum order of lag for ARMA simulation : 4
>> toc
Elapsed time is 36.499133 seconds.
です与えられたコード？私はparforを使って1つのループを実行する必要がありますか？
並列実行のための最初の私が作成したパラレルプール
parpool('local',4)
Starting parallel pool (parpool) using the 'local' profile ... connected to 4 workers.
ans =
Pool with properties:
Connected: true
NumWorkers: 4
Cluster: local
AttachedFiles: {}
IdleTimeout: 30 minute(s) (30 minutes remaining)
SpmdEnabled: true
>> [AR_order,MA_order]=complete_arima();
enter necessary order of differencing : 1
maximum order of lag for ARMA simulation : 4
>> toc
Elapsed time is 24.983676 seconds.
、私は
function [AR_order,MA_order]= complete_arima(Y)
%% if not specific input from user, use default time series
if nargin<1
Y=xlsread('ARIMA','ARIMA','d6:d468');
end
% Y is the observed time series
%% graphical representation of time series
subplot(3,1,1);
plot(Y);
title('original time series');
%%autocorrelation/partial autocorrelation of given signal
subplot(3,1,2);
autocorr(Y);
subplot(3,1,3);
parcorr(Y);
hold off
%% entering necessary parameters for Arima Testing
d=input('enter necessary order of differencing : ');
M=input('maximum order of lag for ARMA simulation : ');
%% simulate ARIMA model on the base of given input
LOGL = zeros(M,M); %Initialize
PQ = zeros(M,M);
parfor p = 1:M
for q = 1:M
mod = arima(p,d,q);% for each pair generate new ARIMA model
[~,~,logL] = estimate(mod,Y,'print',false);
LOGL(p,q) = logL;
PQ(p,q) = p+q;
end
end
%% calculate BIC
LOGL = reshape(LOGL,M*M,1);
PQ = reshape(PQ,M*M,1);
[~,bic] = aicbic(LOGL,PQ+1,100);
bic=reshape(bic,M,M);
%% determine order of ARIMA by finding minimum element from matrix bic and corresponding indeces
[AR_order,MA_order]=find(bic==min(min(bic)));
end
がどのように私はより多くをスピードアップすることができます次のコードを使用？私はベクトル化することができ、コード内の任意の行がありますか？ありがとうございます。