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Time-Series Forecasting with PHP

This PHP 5 class forecasts future values from past data. It takes into account the growth trend, seasonality and irregularity (noise) in input data. This class uses an external class: linear regression analysis

<?php
/** A time-series forcasting, given a series of data (w/ seasonal data), predict future data
* Src: http://home.ubalt.edu/ntsbarsh/stat-data/Forecast.htm & BUS264 (Oliver Yu's coursenote)
* @author Son Nguyen
* @since 10/1/2008
* @package Framework
* @subpackage Math
*/
class CMathForecasting {
   private $mPastDatas; // array of data (eg: x1,x2,x3,x4...)
   private $mNumSeasons; // the number of seasons to consider (eg: 4 quarters/year, 7 days/week, 24 hours/day)
   /** constructor */
   function __construct($pPastDatas,$pNumSeasons) {
      $this->mPastDatas = $pPastDatas;
      $this->mNumSeasons = $pNumSeasons;
   }

/** compute the n-season moving average */
   function computeSMA() {
      $vSMA = array();
      for ($i=0;$i<count($this->mPastDatas);$i++) {
         if ($i+$this->mNumSeasons-1>=count($this->mPastDatas)) { // out of bound, done
            break;
         } // fi

$vSum = 0;
         for ($j=0;$j<$this->mNumSeasons;$j++) {
            $vSum += $this->mPastDatas[$i+$j];
         } // rof
         $vSMA[] = $vSum/$this->mNumSeasons;
      } // rof
      return $vSMA;
   }

/** compute centered moving average from the n-season moving average */
   function computeCMA($pSMA) {
      $vCMA = array();
      for ($i=0;$i<count($pSMA);$i++) {
         if ($i+1>=count($pSMA)) { // out of bound, done
            break;
         } // fi
         $vCMA[] = ($pSMA[$i]+$pSMA[$i+1])/2;
      } // rof
      return $vCMA;
   }

/** season irregularity */
   function computeNoises($pCMA) {
      $vNoises = array();
      for ($i=0;$i<count($pCMA);$i++) {
         $vStarting = floor($this->mNumSeasons/2);
         $vNoises[] = $this->mPastDatas[$i+$vStarting]/$pCMA[$i];
      } // rof
      return $vNoises;
   }

/** comment */
   function computeSeasonIndices($pNoises) {
      $vIndices = array();
      for ($i=0;$i<$this->mNumSeasons;$i++) {
         $vSum = array();
         for ($j=$i;$j<count($pNoises);$j+=$this->mNumSeasons) {
            $vSum[] = $pNoises[$j];
         } // rof
         $vStarting = (floor($this->mNumSeasons/2)+$i)%$this->mNumSeasons;
         $vIndices[$vStarting] = array_sum($vSum)/count($vSum);
      } // rof

ksort($vIndices);

// also adjust these season indices
      $vSum = array_sum($vIndices);
      for ($i=0;$i<count($vIndices);$i++) {
         $vIndices[$i] = $vIndices[$i]*$this->mNumSeasons/$vSum;
      } // rof
      return $vIndices;
   }

/** comment */
   function computeDeSeasonalized($pSeasonIndex) {
      $vDatas = array();

for ($i=0;$i<count($this->mPastDatas);$i++) {
         $vDatas[] = $this->mPastDatas[$i]/$pSeasonIndex[$i%$this->mNumSeasons];
      } // rof
      return $vDatas;
   }

/** how many future periods to predict */
   function predict($pNumFuturePeriods) {
      $vSMA = $this->computeSMA();
      if ($this->mNumSeasons%2==0) { // even
         $vCMA = $this->computeCMA($vSMA);
      } else { // odd, nSMA=CMA
         $vCMA = $vSMA;
      } // fi

$vNoises = $this->computeNoises($vCMA);
      $vIndices = $this->computeSeasonIndices($vNoises);

$vDeSeasonalized = $this->computeDeSeasonalized($vIndices);

// perform regression to get the trend line
      $vRegression = new CRegressionLinear($vDeSeasonalized);
      list($vXVar,$vIntercept) = $vRegression->calculate();

$vForecast = array();
      for ($i=0;$i<$pNumFuturePeriods;$i++) {
         $vForecast[] = $vIntercept + $vXVar*(count($this->mPastDatas)+$i);
      } // rof

// have to re-seasonalized these values
      for ($i=0;$i<count($vForecast);$i++) {
         $vForecast[$i] = $vForecast[$i]*$vIndices[(count($this->mPastDatas)+$i)%$this->mNumSeasons];
      } // rof

return $vForecast;

}
}
?>

Sample Usage

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Time-Series Forecasting with PHP

Sample Usage

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