ARIMA实现（亲测可用）

需要jar包Jama-1.0.2.jar，数据：时序数据的值下载连接
https://download.csdn.net/download/dongyang1124/86265504

package arima;import java.util.Vector;public class ARMAModel
{private double [] data = {};private int p;        //AR阶数private int q;        //MA阶数public ARMAModel(double [] data, int p, int q){this.data = data;this.p = p;this.q = q;}/*** 在ARMA模型中，首先根据原始数据求得AR模型的自回归系数(AR系数)* 利用AR系数与原始数据，求解的残差序列，根据残差序列的自协方差最终求得ARMA中MA系数* @return ar, ma*/public Vector<double []> solveCoeOfARMA(){Vector<double []>vec = new Vector<>();//ARMA模型double [] armaCoe = new ARMAMethod().computeARMACoe(this.data, this.p, this.q);//AR系数double [] arCoe = new double[this.p + 1];System.arraycopy(armaCoe, 0, arCoe, 0, arCoe.length);//MA系数double [] maCoe = new double[this.q + 1];System.arraycopy(armaCoe, (this.p + 1), maCoe, 0, maCoe.length);vec.add(arCoe);vec.add(maCoe);return vec;}
}

package arima;import java.util.Vector;public class ARModel
{private double [] data;private int p;public ARModel(double [] data, int p){this.data = data;this.p = p;}public Vector<double []> solveCoeOfAR(){Vector<double []>vec = new Vector<>();double [] arCoe = new ARMAMethod().computeARCoe(this.data, this.p);vec.add(arCoe);return vec;}
}

package arima;import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.text.SimpleDateFormat;
import java.util.ArrayList;
import java.util.Date;public class MainTest {private static final SimpleDateFormat sdfWhole = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");public static void main(String args[]) {Path path = Paths.get("./data/", "data_20220621.txt");File file = path.toFile();try (BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream(file)))) {String line = null;ArrayList<Double> al = new ArrayList<Double>();while ((line = br.readLine()) != null) {al.add(Double.parseDouble(line));}ArrayList<Double> samplingDataList = new ArrayList<>();ArrayList<Double> comparisonDataList = new ArrayList<>();System.out.println("开始时间"+sdfWhole.format(new Date()));for (int i = 0; i < al.size(); i++) {if (i < al.size() / 3 * 2) {samplingDataList.add(al.get(i));} else {comparisonDataList.add(al.get(i));}}for (double comData : comparisonDataList) {double predict = predect(samplingDataList);//System.out.println("Predict value=" + predict);samplingDataList.add(predict);/*System.out.println("Predict error=" + (predict - comData)/ comData * 100 + "%");*/}System.out.println("结束时间"+sdfWhole.format(new Date()));} catch (FileNotFoundException fnfe) {fnfe.printStackTrace();} catch (IOException ioe) {ioe.printStackTrace();}}/*** 获取预测结果的值* * @param al* @return*/public static double predect(ArrayList<Double> al) {double[] data = null;data = al.stream().mapToDouble(i -> i).toArray();ARIMAModel arima = new ARIMAModel(data);ArrayList<int[]> list = new ArrayList<>();int period = 7;int modelCnt = 3, cnt = 0;// 通过多次预测的平均值作为预测值int[] tmpPredict = new int[modelCnt];for (int k = 0; k < modelCnt; ++k) {// 控制通过多少组参数进行计算最终的结果int[] bestModel = arima.getARIMAModel(period, list,(k == 0) ? false : true);if (bestModel.length == 0) {tmpPredict[k] = (int) data[data.length - period];cnt++;break;} else {int predictDiff = arima.predictValue(bestModel[0],bestModel[1], period);tmpPredict[k] = arima.aftDeal(predictDiff, period);cnt++;}list.add(bestModel);}double sumPredict = 0.0;for (int k = 0; k < cnt; ++k) {sumPredict += (double) tmpPredict[k] / (double) cnt;}double predict = (double) Math.round(sumPredict);return predict;}
}

package arima;import java.util.Vector;public class MAModel
{private double [] data;private int q;public MAModel(double [] data, int q){this.data = data;this.q = q;}public Vector<double []> solveCoeOfMA(){Vector<double []>vec = new Vector<>();double [] maCoe = new ARMAMethod().computeMACoe(this.data, this.q);vec.add(maCoe);return vec;}
}

package arima;import java.util.ArrayList;
import java.util.Random;
import java.util.Vector;public class ARIMAModel
{double [] originalData = {};double [] dataFirDiff = {};Vector<double []>arimaCoe = new Vector<>();public ARIMAModel(){}public ARIMAModel(double [] originalData){this.originalData = originalData;}public double [] preFirDiff(double [] preData)      //一阶差分(1){  double [] tmpData = new double[preData.length - 1];for (int i = 0; i < preData.length - 1; ++i){tmpData[i] = preData[i + 1] - preData[i];}return tmpData;}public double [] preSeasonDiff(double [] preData)    //季节性差分(6, 7){  double [] tmpData = new double[preData.length - 7];for (int i = 0; i < preData.length - 7; ++i){tmpData[i] = preData[i + 7] - preData[i];}return tmpData;}public double [] preDealDiff(int period){if (period >= originalData.length - 1)      // 将6也归为季节性差分{period = 0;}switch (period){case 0:return this.originalData;case 1:       this.dataFirDiff = this.preFirDiff(this.originalData);return this.dataFirDiff;default:    return preSeasonDiff(originalData);}}public int [] getARIMAModel(int period, ArrayList<int []>notModel, boolean needNot){double [] data = this.preDealDiff(period);double minAIC = Double.MAX_VALUE;int [] bestModel = new int[3];int type = 0;Vector<double []>coe = new Vector<>();// model产生, 即产生相应的p, q参数int len = data.length;if (len > 5){len = 5;}int size = ((len + 2) * (len + 1)) / 2 - 1;int [][] model = new int[size][2];int cnt = 0;for (int i = 0; i <= len; ++i){for (int j = 0; j <= len - i; ++j){if (i == 0 && j == 0)continue;model[cnt][0] = i;model[cnt++][1] = j;}}for (int i = 0; i < model.length; ++i){// 控制选择的参数boolean token = false;if (needNot){for (int k = 0; k < notModel.size(); ++k){if (model[i][0] == notModel.get(k)[0] && model[i][1] == notModel.get(k)[1]){token = true;break;}}}if (token){continue;}if (model[i][0] == 0){MAModel ma = new MAModel(data, model[i][1]);coe = ma.solveCoeOfMA();type = 1;}else if (model[i][1] == 0){ARModel ar = new ARModel(data, model[i][0]);coe = ar.solveCoeOfAR();type = 2;}else{ARMAModel arma = new ARMAModel(data, model[i][0], model[i][1]);coe = arma.solveCoeOfARMA();type = 3;}                double aic = new ARMAMethod().getModelAIC(coe, data, type);// 在求解过程中如果阶数选取过长，可能会出现NAN或者无穷大的情况if (Double.isFinite(aic) && !Double.isNaN(aic) && aic < minAIC){minAIC = aic;bestModel[0] = model[i][0];bestModel[1] = model[i][1];bestModel[2] = (int)Math.round(minAIC);this.arimaCoe = coe;}}return bestModel;}public int aftDeal(int predictValue, int period){if (period >= originalData.length){period = 0;}switch (period){case 0:return (int)predictValue;case 1:return (int)(predictValue + originalData[originalData.length - 1]);case 2:default:  return (int)(predictValue + originalData[originalData.length - 7]);}}public int predictValue(int p, int q, int period){double [] data = this.preDealDiff(period);int n = data.length;int predict = 0;double tmpAR = 0.0, tmpMA = 0.0;double [] errData = new double[q + 1];Random random = new Random();if (p == 0){if(null!=this.arimaCoe&&!this.arimaCoe.isEmpty()){double [] maCoe = this.arimaCoe.get(0);for(int k = q; k < n; ++k){tmpMA = 0;for(int i = 1; i <= q; ++i){tmpMA += maCoe[i] * errData[i];}//产生各个时刻的噪声for(int j = q; j > 0; --j){errData[j] = errData[j - 1];}errData[0] = random.nextGaussian()*Math.sqrt(maCoe[0]);}}predict = (int)(tmpMA); }else if (q == 0){double [] arCoe = this.arimaCoe.get(0);for(int k = p; k < n; ++k){tmpAR = 0;for(int i = 0; i < p; ++i){tmpAR += arCoe[i] * data[k - i - 1];}}predict = (int)(tmpAR);}else{double [] arCoe = this.arimaCoe.get(0);double [] maCoe = this.arimaCoe.get(1);for(int k = p; k < n; ++k){tmpAR = 0;tmpMA = 0;for(int i = 0; i < p; ++i){tmpAR += arCoe[i] * data[k- i - 1];}for(int i = 1; i <= q; ++i){tmpMA += maCoe[i] * errData[i];}//产生各个时刻的噪声for(int j = q; j > 0; --j){errData[j] = errData[j-1];}errData[0] = random.nextGaussian() * Math.sqrt(maCoe[0]);}predict = (int)(tmpAR + tmpMA);}return predict;}
}

package arima;import java.util.Random;
import java.util.Vector;import Jama.Matrix;public class ARMAMethod
{public ARMAMethod(){}/*** @param originalData* @return 均值*/public double avgData(double [] originalData){return this.sumData(originalData) / originalData.length;}/*** @param originalData* @return 求和*/public double sumData(double [] originalData){double sum = 0.0;for (int i = 0; i < originalData.length; ++i){sum += originalData[i];}return sum;}/*** 计算标准差 sigma = sqrt(var);* @param originalData* @return 标准差*/public double stdErrData(double [] originalData){return Math.sqrt(this.varErrData(originalData));}/*** 计算方差 var = sum(x - mu) ^2 / N;* @param originalData* @return 方差*/public double varErrData(double [] originalData){if (originalData.length <= 1)return 0.0;double var = 0.0;double mu = this.avgData(originalData);for (int i = 0; i < originalData.length; ++i){var += (originalData[i] - mu) * (originalData[i] - mu);}var /= (originalData.length - 1);       //方差的无偏估计return var;}/*** 计算自相关函数(系数) rou(k) = C(k) / C(0);* 其中 C(k) = sum((x(t) - mu)*(x(t - k) - mu)) / (N - k),* C(0) = var =  sum(x(t) - mu) ^2 / N;* @param originalData* @param order* @return 自相关函数(rou(k))*/public double [] autoCorrData(double [] originalData, int order){double [] autoCov = this.autoCovData(originalData, order);double [] autoCorr = new double[order + 1];        //默认初始化为0double var = this.varErrData(originalData);if (var != 0){for (int i = 0; i < autoCorr.length; ++i){autoCorr[i] = autoCov[i] / var;}}return autoCorr;}/*** @param dataFir* @param dataSec* @return 皮尔逊相关系数(互相关)*/public double mutalCorr(double [] dataFir, double [] dataSec){double sumX = 0.0;double sumY = 0.0;double sumXY = 0.0;double sumXSq = 0.0;double sumYSq = 0.0;int len = 0;if (dataFir.length != dataSec.length){len = Math.min(dataFir.length, dataSec.length);}else{len = dataFir.length;}for (int i = 0; i < len; ++i){sumX += dataFir[i];sumY += dataSec[i];sumXY += dataFir[i] * dataSec[i];sumXSq += dataFir[i] * dataFir[i];sumYSq += dataSec[i] * dataSec[i];}      double numerator = sumXY - sumX * sumY / len;double denominator = Math.sqrt((sumXSq - sumX * sumX / len) * (sumYSq - sumY * sumY / len));if (denominator == 0){return 0.0;}return numerator/ denominator;}/*** @param data* @return       互相关矩阵*/public double [][] computeMutalCorrMatrix(double [][] data){double [][] result = new double[data.length][data.length];for (int i = 0; i < data.length; ++i){for (int j = 0; j < data.length; ++j){result[i][j] = this.mutalCorr(data[i], data[j]);}}return result;}/*** 计算自协方差，C(k) = sum((x(t) - mu)*(x(t - k) - mu)) / (N - k);* @param originalData* @param order* @return 自协方差(gama(k))-->认为是自相关系数*/public double [] autoCovData(double [] originalData, int order){double mu = this.avgData(originalData);double [] autoCov = new double[order + 1];for (int i = 0; i <= order; ++i){autoCov[i] = 0.0;for (int j = 0; j < originalData.length - i; ++j){autoCov[i] += (originalData[i + j] - mu) * (originalData[j] - mu);}autoCov[i] /= (originalData.length - i);}return autoCov;}/*** @param vec       模型的系数* @param data     数据* @param type        选定的模型* @return*/public double getModelAIC(Vector<double []>vec, double [] data, int type){int n = data.length;int p = 0, q = 0;double tmpAR = 0.0, tmpMA = 0.0;double sumErr = 0.0;Random random = new Random();/* MA */  if (type == 1){double [] maCoe = vec.get(0);q = maCoe.length;double [] errData = new double[q];for (int i = q - 1; i < n; ++i){tmpMA = 0.0;for (int j = 1; j < q; ++j){tmpMA += maCoe[j] * errData[j];}for (int j = q - 1; j > 0; --j){errData[j] = errData[j - 1];}errData[0] = random.nextGaussian() * Math.sqrt(maCoe[0]);sumErr += (data[i] - tmpMA) * (data[i] - tmpMA);}// return Math.log(sumErr) + (q + 1) * 2 / n;return (n - (q - 1)) * Math.log(sumErr / (n - (q - 1))) + (q + 1) * 2;// return  (n-(q-1))*Math.log(sumErr/(n-(q-1)))+(q)*Math.log(n-(q-1));        //AIC 最小二乘估计}/* AR */else if (type == 2){double [] arCoe = vec.get(0);p = arCoe.length;for (int i = p - 1; i < n; ++i){tmpAR = 0.0;for (int j = 0; j < p - 1; ++j){tmpAR += arCoe[j] * data[i - j - 1];}sumErr += (data[i] - tmpAR) * (data[i] - tmpAR);}
//          return Math.log(sumErr) + (p + 1) * 2 / n;return (n - (p - 1)) * Math.log(sumErr / (n - (p - 1))) + (p + 1) * 2;// return (n-(p-1))*Math.log(sumErr/(n-(p-1)))+(p)*Math.log(n-(p-1));      //AIC 最小二乘估计}/* ARMA */else{double [] arCoe = vec.get(0);double [] maCoe = vec.get(1);p = arCoe.length;q = maCoe.length;double [] errData = new double[q];for (int i = p - 1; i < n; ++i){tmpAR = 0.0;for (int j = 0; j < p - 1; ++j){tmpAR += arCoe[j] * data[i - j - 1];}tmpMA = 0.0;for (int j = 1; j < q; ++j){tmpMA += maCoe[j] * errData[j];}for (int j = q - 1; j > 0; --j){errData[j] = errData[j - 1];}errData[0] = random.nextGaussian() * Math.sqrt(maCoe[0]);sumErr += (data[i] - tmpAR - tmpMA) * (data[i] - tmpAR - tmpMA);}
//          return Math.log(sumErr) + (q + p + 1) * 2 / n;return (n - (q + p - 1)) * Math.log(sumErr / (n - (q + p - 1))) + (p + q) * 2;// return (n-(p-1))*Math.log(sumErr/(n-(p-1)))+(p+q-1)*Math.log(n-(p-1));      //AIC 最小二乘估计}}// Y-W方程求解/*** @param garma  代表的是数据的协方差* @return 返回经由Y-W方程求解的结果，其中最后数组的最后一个元素存储的是模型中的噪声方差*/public double [] YWSolve(double [] garma){int order = garma.length - 1;double [] garmaPart = new double[order];System.arraycopy(garma, 1, garmaPart, 0, order);// 将协方差转换为矩阵的形式double [][] garmaArray = new double[order][order];for (int i = 0; i < order; ++i){// 对角线garmaArray[i][i] = garma[0];//下三角int subIndex = i;for (int j = 0; j < i; ++j){garmaArray[i][j] = garma[subIndex--];}//上三角int topIndex = i;for (int j = i + 1; j < order; ++j){garmaArray[i][j] = garma[++topIndex];}}/* 调用了juma包，其实现了大部分对矩阵的操作 *//* 可能会存在矩阵不可逆的情况，在矩阵不可逆时可以通过将对角线元素全部增加1e-6做修正 */Matrix garmaMatrix = new Matrix(garmaArray);               Matrix garmaMatrixInverse = garmaMatrix.inverse();Matrix autoReg = garmaMatrixInverse.times(new Matrix(garmaPart, order));double [] result = new double[autoReg.getRowDimension() + 1];for (int i = 0; i < autoReg.getRowDimension(); ++i){result[i] = autoReg.get(i, 0);}double sum = 0.0;for (int i = 0; i < order; ++i){sum += result[i] * garma[i];}result[result.length - 1] = garma[0] - sum;return result;}// Levinson 方法求解/*** @param garma  代表的是数据的协方差* @return 返回结果的第一行元素代表的是在迭代过程中的方差，* 其余的元素代表的是迭代过程中存储的系数*/public double [][] LevinsonSolve(double [] garma){int order = garma.length - 1;double [][] result = new double[order + 1][order + 1];double [] sigmaSq = new double[order + 1];sigmaSq[0] = garma[0];result[1][1] = garma[1] / sigmaSq[0];sigmaSq[1] = sigmaSq[0] * (1.0 - result[1][1] * result[1][1]);for (int k = 1; k < order; ++k){double sumTop = 0.0;double sumSub = 0.0;for (int j = 1; j <= k; ++j){sumTop += garma[k + 1 - j] * result[k][j];sumSub += garma[j] * result[k][j];}result[k + 1][k + 1] = (garma[k + 1] - sumTop) / (garma[0] - sumSub);for (int j = 1; j <= k; ++j){result[k + 1][j] = result[k][j] - result[k + 1][k + 1] * result[k][k + 1 - j];} sigmaSq[k + 1] = sigmaSq[k] * (1.0 - result[k + 1][k + 1] * result[k + 1][k + 1]);}result[0] = sigmaSq;return result;}// 求解AR(p)的系数/*** @param originalData  原始数据* @param p     模型的阶数* @return     AR模型的系数*/public double [] computeARCoe(double [] originalData, int p){double [] garma = this.autoCovData(originalData, p);     //p+1double [][] result = this.LevinsonSolve(garma);      //(p + 1) * (p + 1)double [] ARCoe = new double[p + 1];for (int i = 0; i < p; ++i){ARCoe[i] = result[p][i + 1];}ARCoe[p] = result[0][p];       //噪声参数//         return this.YWSolve(garma);return ARCoe;}// 求解MA(q)的系数/*** @param originalData   原始数据* @param q          模型阶数* @return          MA系数*/public double [] computeMACoe(double [] originalData, int q){// 确定最佳的p
//      int p = 0;
//      double minAIC = Double.MAX_VALUE;
//      int len = originalData.length;
//      for (int i = 1; i < len; ++i)
//      {
//          double [] garma = this.autoCovData(originalData, i);
//          double [][] result = this.LevinsonSolve(garma);
//
//          double [] ARCoe = new double[i + 1];
//          for (int k = 0; k < i; ++k)
//          {
//              ARCoe[k] = result[i][k + 1];
//          }
//          ARCoe[i] = result[0][i];double [] ARCoe = this.YWSolve(garma);
//
//          Vector<double []> vec = new Vector<>();
//          vec.add(ARCoe);
//          double aic = this.getModelAIC(vec, originalData, 2);
//          if (aic < minAIC)
//          {
//              minAIC = aic;
//              p = i;
//          }
//      }int p = (int)Math.log(originalData.length);//     System.out.println("The best p is " + p);// 求取系数double [] bestGarma = this.autoCovData(originalData, p);double [][] bestResult = this.LevinsonSolve(bestGarma);double [] alpha = new double[p + 1];alpha[0] = -1;for (int i = 1; i <= p; ++i){alpha[i] = bestResult[p][i];}//       double [] result = this.YWSolve(bestGarma);
//      double [] alpha = new double[p + 1];
//      alpha[0] = -1;
//      for (int i = 1; i <= p; ++i)
//      {
//          alpha[i] = result[i - 1];
//      }double [] paraGarma = new double[q + 1];for (int k = 0; k <= q; ++k){double sum = 0.0;for (int j = 0; j <= p - k; ++j){sum += alpha[j] * alpha[k + j];}paraGarma[k] = sum / bestResult[0][p];}double [][] tmp = this.LevinsonSolve(paraGarma);double [] MACoe = new double[q + 1];for (int i = 1; i < MACoe.length; ++i){MACoe[i] = -tmp[q][i];}MACoe[0] = 1 / tmp[0][q];      //噪声参数//        double [] tmp = this.YWSolve(paraGarma);
//      double [] MACoe = new double[q + 1];
//      System.arraycopy(tmp, 0, MACoe, 1, tmp.length - 1);
//      MACoe[0] = tmp[tmp.length - 1];return MACoe;}// 求解ARMA(p, q)的系数/*** @param originalData       原始数据* @param p         AR模型阶数* @param q           MA模型阶数* @return            ARMA模型系数*/public double [] computeARMACoe(double [] originalData, int p, int q){double [] allGarma = this.autoCovData(originalData, p + q);double [] garma = new double[p + 1];for (int i = 0; i < garma.length; ++i){garma[i] = allGarma[q + i];}double [][] arResult = this.LevinsonSolve(garma);// ARdouble [] ARCoe = new double[p + 1];for (int i = 0; i < p; ++i){ARCoe[i] = arResult[p][i + 1];}ARCoe[p] = arResult[0][p];
//      double [] ARCoe = this.YWSolve(garma);// MAdouble [] alpha = new double[p + 1];alpha[0] = -1;for (int i = 1; i <= p; ++i){alpha[i] = ARCoe[i - 1];}double [] paraGarma = new double[q + 1];for (int k = 0; k <= q; ++k){double sum = 0.0;for (int i = 0; i <= p; ++i){for (int j = 0; j <= p; ++j){sum += alpha[i] * alpha[j] * allGarma[Math.abs(k + i - j)];}}paraGarma[k] = sum;}double [][] maResult = this.LevinsonSolve(paraGarma);double [] MACoe = new double[q + 1];for (int i = 1; i <= q; ++i){MACoe[i] = maResult[q][i];}MACoe[0] = maResult[0][q];//       double [] tmp = this.YWSolve(paraGarma);
//      double [] MACoe = new double[q + 1];
//      System.arraycopy(tmp, 0, MACoe, 1, tmp.length - 1);
//      MACoe[0] = tmp[tmp.length - 1];double [] ARMACoe = new double[p + q + 2];for (int i = 0; i < ARMACoe.length; ++i){if (i < ARCoe.length){ARMACoe[i] = ARCoe[i];}else{ARMACoe[i] = MACoe[i - ARCoe.length];}}return ARMACoe;}
}

ARIMA实现（亲测可用）相关推荐

每日起床前做这五个动作，可保障你终身不感冒——奥运福娃设计师韩美林老师亲测可用
1.醒来后第一件事不是起身,先在床上躺着伸懒腰→四肢拉直了→让经脉拉直了(来回拉三四次) 2.然后做下图的姿势,侧卧→抱膝→弯曲→膝盖顶住胸部→吐气,把所有体内的浊气吐出 3.然后坐起来,双手在脸上的 ...
java正则括号_java正则表达式获取大括号小括号内容并判断数字和小数亲测可用...
获取大括号小括号内容项目开发用到了,暂做个简单记录 private static String regex = "\\{([^}]*)\\}";//匹配大括号 private s ...
Android FFmpeg移植总攻略——获取视频帧数（亲测可用）
第一次尝试使用Android 移植FFmpeg算法,一路坎坷,最终做如下总结,适用于Android手机.Android开发板.亲测可用. 一.下载组件在Android Studio中下载所需组件:C ...
Android 手势返回上一界面亲测可用
在现如今,Android使用越来越便捷,也对Android开发提出更高要求,便捷体现在开发的每一处. 这里介绍Android使用手势返回上一界面.亲测可用! 一.思考: 1. 监听可手势返回上一界面的 ...
VC6.0连接数据库，并把大量数据从ACCess转移到SQL sever2008 的问题（亲测可用）
如果以下有不懂,可以加我微信交流xpj13098878561 VC6.0连接数据库,并把大量数据从ACCess转移到SQL sever2008 的问题(亲测可用) VC6.0下载连接https://p ...
linux samba默认端口,Linux 指定端口挂载samba 亲测可用！
我在云主机开启了445,,46端口通过tcptraceroute可以看到,运营商屏蔽了访问445 samba 端口 chunli@ubuntu ~$ tcptraceroute 47.88.159. ...
springboot心跳检测_springboot websocket 实时刷新添加心跳机制（亲测可用版）
思路在我之前的一篇文章当中写到了websocket的实时刷新,但是有个问题没有解决,就是长时间没有数据的时候,这个连接就会自动断开,然后再次进行连接的话,需要再次进行连接.如果加入心跳机制的话,10 ...
js正则检测输入内容为数字,包括负数,整数,浮点数,亲测可用.JS检测整数,js检测浮点数,js检测负数,js检测数字,js限制输入框只输入数字,解决H5 number类型只能输入正数问题.
JS 正则实现数字检测和限制输入,包括整数.浮点数.负数(亲测可用) 网上搜了一下,居然可以直接拿来用的很少,要么就是只能检测正整数.要么是只能检测浮点数,总之很多都不适合,而H5提供的type=&q ...
php语法高亮编辑器,最新PHPCMS V9编辑器代码高亮显示亲测可用(提前格式化)
最近PHPCMS V9更新挺频繁的,是好事,让我们更完美的用它的功能,是坏事,以前的教程都不能用了.譬如这篇在PHPCMS V9文章中实现代码高亮显示的功能,之前的教程已经不能用了,今天站长莪叆啰有空 ...
PHP｜Yii2下通过插件进行图片处理（亲测可用）
开源的力量! Step by step,可以安装好Yii2的图片处理插件. 亲测可用. 参考 http://www.yiichina.com/tutor...

ARIMA实现（亲测可用）

ARIMA实现（亲测可用）相关推荐

最新文章

热门文章