Time Series Forecast (TSF)

TimeSeriesForecast.efs  
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File Name: TimeSeriesForecast.efs

Description:
Time Series Forecast (TSF)

Formula Parameters:

• nLength : 5
• nBarPlus : 7

Notes:
This technical indicator is based on linear regression analysis.
The value of TSF for each bar is based on a regression analysis of the preceding N bars. N is called the regression period in the setup window for TSF. The user specifies a forecast period F. F is used to derive a predicted (forecasted) price value F periods in the future based on the slope of the regression line for the preceding N periods.

Download File:
TimeSeriesForecast.efs


EFS Code:

/*********************************
Provided By:  
    eSignal (Copyright c eSignal), a division of Interactive Data 
    Corporation. 2009. All rights reserved. This sample eSignal 
    Formula Script (EFS) is for educational purposes only and may be 
    modified and saved under a new file name.  eSignal is not responsible
    for the functionality once modified.  eSignal reserves the right 
    to modify and overwrite this EFS file with each new release.

Description:        
    Time Series Forecast (TSF) 
    
Version:            1.0  05/12/2009
    
Formula Parameters:                     Default:
    nLength                             5
    nBarPlus                            7

Notes:
    This technical indicator is based on linear regression analysis. 
    The value of TSF for each bar is based on a regression analysis 
    of the preceding N bars. N is called the regression period in 
    the setup window for TSF. The user specifies a forecast period 
    F. F is used to derive a predicted (forecasted) price value F 
    periods in the future based on the slope of the regression line 
    for the preceding N periods.

**********************************/
var fpArray = new Array();
var bInit = false;

function preMain() {
    setPriceStudy(true);    
    setStudyTitle("Time Series Forecast");
    setCursorLabelName("TSF");
    var x = 0;
    fpArray[x] = new FunctionParameter("nLength", FunctionParameter.NUMBER);
    with(fpArray[x++]) {
        setLowerLimit(1);
        setDefault(5);
    }    
    fpArray[x] = new FunctionParameter("nBarPlus", FunctionParameter.NUMBER);
    with(fpArray[x++]) {
        setLowerLimit(1);
        setDefault(7);
    }        
}

var xTimeSeriesForecast = null;

function main(nLength, nBarPlus) {
var nBarState = getBarState();
var nTimeSeriesForecast = 0;
    if (nBarState == BARSTATE_ALLBARS) {
        if (nLength == null) nLength = nLength;
        if (nBarPlus == null) nLength = nBarPlus;
    }    
    if (bInit == false) {
        xTimeSeriesForecast = efsInternal("Calc_TSF", nLength, nBarPlus);
        bInit = true;
    }
    nTimeSeriesForecast = xTimeSeriesForecast.getValue(0);
    if (nTimeSeriesForecast == null) return;
    return nTimeSeriesForecast;
}

var xClose = null;
var bSecondInit = false;

function Calc_TSF(nLength, nBarPlus) {
var SL = 0;
var TSF = 0;
var SumBars = nLength * (nLength - 1) * 0.5;
var SumSqrBars = (nLength - 1) * nLength * (2 * nLength - 1) / 6;
var Sum1 = 0;
var SumY = 0;
var i = 0;
var Slope = 0;
var Intercept = 0;
    if (getCurrentBarCount() <= nLength) return;
    if(bSecondInit == false){
        xClose = close();
        bSecondInit = true;
    }
    for (i = 0; i < nLength; i++) {
        Sum1 += i * xClose.getValue(-i);
        SumY += xClose.getValue(-i);
    }
    var Sum2 = SumBars * SumY;
    var Num1 = nLength * Sum1 - Sum2;
    var Num2 = SumBars * SumBars - nLength * SumSqrBars;
    if (Num2 == 0) return;
    Slope = Num1 / Num2;
    SL = Num1 / Num2;
	Intercept = (SumY - Slope * SumBars) / nLength;
    TSF = Intercept + Slope * (nLength - 1 - nBarPlus);     
    return TSF;
}