在Java中为ARIMA调用R

我尝试在JAVA中调用R来实现ARIMA函数。第一个示例运行arima（）。它工作得很好

Rengine rengine = new Rengine(new String[]{"--vanilla"},false,null);
System.out.println("  *BEGIN*  ");
rengine.eval("library(forecast)");
double[] trainingData = = {10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422,
            + 6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
            + 10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
            + 12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
            + 16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
            + 12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
            + 15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947};
rengine.assign("value", trainingData);
rengine.eval("traindatats<-ts(value)");
rengine.eval("loopMinLag<-0");
rengine.eval("loopMaxLag<-8");
rengine.eval("best.order <- c(loopMinLag, 1, loopMinLag)");
rengine.eval("best.aic = Inf");
rengine.eval("for(P in loopMinLag:loopMaxLag) { for(Q in loopMinLag:loopMaxLag) { myModel <- arima(traindatats, order=c(P,1,Q), method='ML'); fit.aic <- myModel$aic;  if(fit.aic < best.aic) { best.order <- c(P, 1, Q);  best.aic <- fit.aic; }  } }");
rengine.eval("model <- list()");
rengine.eval("model$P <- best.order[1]");
rengine.eval("model$D <- 1");
rengine.eval("model$Q <- best.order[3]");
rengine.eval("ar<-arima(traindatats,order=c(model$P,model$D,model$Q))");
rengine.eval("f<-predict(ar,n.ahead=7)");
System.out.println("  *RESULT*  ");
double forecast[] = new double[7];
for (int i = 0,j=1; i < 7; i++,j++) {
          forecast[i] = rengine.eval("f$pred[" + j + "]").asDouble();
          System.out.print(" "+forecast[i]);
      }
System.out.println("\r\n  *DONE* ");

Rengine-Rengine=new-Rengine（新字符串[]{--vanilla}，false，null）；
System.out.println（“*BEGIN*”）；
rengine.eval（“库（预测）”）；
双[]训练数据=={10930103181059510972770667592921055177222109131115181866422，
+ 6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
+ 10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
+ 12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
+ 16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
+ 12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
+ 15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947};
重新设计分配（“值”，培训数据）；
重新发动机评估（“此处的列车数据：

rengine.eval("f$pred[" + j + "]").asDouble();

rengine.eval（“f$pred[“+j+”]）
此项为空

根据：

评估

公共REXP eval（java.lang.String s）

看起来，右边好像出了点问题

嗯，也许你对图书馆有一些问题，等等

如果我安装forecast

install.packages('forecast')

在R中，我这样调用代码：

javac -cp /Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri/JRI.jar SimpleR.java
java -Djava.library.path=/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri -cp .:/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri/JRI.jar SimpleR
  *BEGIN*
Forecast: 0
Result: 14436.670778092368
Forecast: 1
Result: 14104.789779948998
Forecast: 2
Result: 14104.789779948998
Forecast: 3
Result: 14104.789779948998
Forecast: 4
Result: 14104.789779948998
  *DONE*

我得到了正确的输出。我的SimpleR.java如下所示：

import org.rosuda.JRI.*;

public class SimpleR {

  public static void main(String [] arg) {
    Rengine rengine = new Rengine(new String[]{"--vanilla"},false,null);
    System.out.println("  *BEGIN*  ");
    // enable R log
    rengine.eval("log<-file('/tmp/file.log')");
    rengine.eval("sink(log, append=TRUE)");
    rengine.eval("sink(log, append=TRUE, type='message')");
    rengine.eval("library(forecast)");
    double[] trainingData =  {10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422,
            + 6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
            + 10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
            + 12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
            + 16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
            + 12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
            + 15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947};
    rengine.assign("value", trainingData);
    rengine.eval("traindatats<-ts(value)");
    rengine.eval("ar<-auto.arima(traindatats)");
    REXP result = rengine.eval("f<-predict(ar,n.ahead=" + 5 + ")");
    if(result == null) {
      System.out.println("Error");
    }
    double forecast[] = new double[5];
    for (int i = 0;i < 5;i++) {
      System.out.println("Forecast: " + i);
      forecast[i] = rengine.eval("f$pred[" + (i+1) + "]").asDouble();
      System.out.println("Result: " + forecast[i]);
    }
    System.out.println("  *DONE*  ");
  }
}

import org.rosuda.JRI.*；
公共类更简单{
公共静态void main（字符串[]arg）{
Rengine-Rengine=new-Rengine（新字符串[]{--香草“}，false，null）；
System.out.println（“*BEGIN*”）；
//启用R日志
rengine.eval（“logBut，如果下面的代码直接在R上运行，没有任何问题。
值非常感谢！但是SimpleR.java对我不起作用。结果是，`*BEGIN*`
错误
预测：0
线程“main”java.lang.NullPointerException
`at SimpleR.main（SimpleR.java:26）`我发现了一个异常情况。在JAVA中，当我调用arima（）时，它会运行几秒钟。然后，它会正确地生成输出。但是，当我在JAVA中调用auto.arima（）时，它运行得非常快。然后，它会输出错误消息。尝试使用log（查看源代码）。这样，您就可以看到R端发生了什么；）我遵循你的建议。我想JRI没有正确配置，尽管我可以在JAVA中调用arima（）R函数。
javac -cp /Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri/JRI.jar SimpleR.java
java -Djava.library.path=/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri -cp .:/Library/Frameworks/R.framework/Versions/3.5/Resources/library/rJava/jri/JRI.jar SimpleR
  *BEGIN*
Forecast: 0
Result: 14436.670778092368
Forecast: 1
Result: 14104.789779948998
Forecast: 2
Result: 14104.789779948998
Forecast: 3
Result: 14104.789779948998
Forecast: 4
Result: 14104.789779948998
  *DONE*

import org.rosuda.JRI.*;

public class SimpleR {

  public static void main(String [] arg) {
    Rengine rengine = new Rengine(new String[]{"--vanilla"},false,null);
    System.out.println("  *BEGIN*  ");
    // enable R log
    rengine.eval("log<-file('/tmp/file.log')");
    rengine.eval("sink(log, append=TRUE)");
    rengine.eval("sink(log, append=TRUE, type='message')");
    rengine.eval("library(forecast)");
    double[] trainingData =  {10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422,
            + 6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355,10477,
            + 10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767,12136,12812,
            + 12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232,13261,13230,15535,
            + 16837,19598,14823,11622,19391,18177,19994,14723,15694,13248,9543,12872,13101,15053,
            + 12619,13749,10228,9725,14729,12518,14564,15085,14722,11999,9390,13481,14795,15845,
            + 15271,14686,11054,10395,14775,14618,16029,15231,14246,12095,10473,15323,15381,14947};
    rengine.assign("value", trainingData);
    rengine.eval("traindatats<-ts(value)");
    rengine.eval("ar<-auto.arima(traindatats)");
    REXP result = rengine.eval("f<-predict(ar,n.ahead=" + 5 + ")");
    if(result == null) {
      System.out.println("Error");
    }
    double forecast[] = new double[5];
    for (int i = 0;i < 5;i++) {
      System.out.println("Forecast: " + i);
      forecast[i] = rengine.eval("f$pred[" + (i+1) + "]").asDouble();
      System.out.println("Result: " + forecast[i]);
    }
    System.out.println("  *DONE*  ");
  }
}

rengine.eval("log<-file('/tmp/file.log')");
rengine.eval("sink(log, append=TRUE)");
rengine.eval("sink(log, append=TRUE, type='message')");