public static (double[], double[]) Calculate(double[] inputValue, int amountOfPeriodsToCalculate)
{
Name = "Promedio simple";
//Call function to calculate the 'n' next periods
double[] full = SimpleAverageHelper.Calculate(
inputValue.ToList(), inputValue.Sum(), amountOfPeriodsToCalculate).ToArray <double>();
//Check if a value is less than 0
if (full.Where(f => f < 0).Count() > 0)
{
Name = "!!!" + Name;
}
return(ArrayBased.Split(full, amountOfPeriodsToCalculate));
}
public static (double[], double[]) Calculate(double[] inputValue, double smoothingConstant)
{
if (0 == inputValue.Length)
{
throw new EmptyParameterArray();
}
Name = string.Format("Suavización exponencial simple | cte. de suavización: {0}", smoothingConstant);
double[] full = SimpleExponentialSmoothingHelper.Calculate(
inputValue.ToList(), inputValue.Take(1).ToList <double>(), smoothingConstant).ToArray();
//Check if a value is less than 0
if (full.Where(f => f < 0).Count() > 0)
{
Name = "!!!" + Name;
}
return(ArrayBased.Split(full, 1));
}
public static (double[], double[]) Calculate(double[] inputValue, int amountOfPeriodsToCalculate)
{
Name = "Simple Linear Regression";
int inputLength = inputValue.Length;
double[] xData = Enumerable.Range(0, inputLength).Select(i => (double)i).ToArray();
Tuple <double, double> parameters = Fit.Line(xData, inputValue);
double[] full = Enumerable.Range(0, inputLength + amountOfPeriodsToCalculate)
.Select(i => parameters.Item1 + parameters.Item2 * i).ToArray();
//Check if a value is less than 0
if (full.Where(f => f < 0).Count() > 0)
{
Name = "!!!" + Name;
}
return(ArrayBased.Split(full, amountOfPeriodsToCalculate));
}
public static (double[], double[]) Calculate(double[] inputValue, int movingAverageTerms)
{
if (0 > movingAverageTerms)
{
throw new NegativeMovingAverageTerms();
}
if (inputValue.Length < movingAverageTerms)
{
return(new double[0], new double[0]); // throw new MovingAverageTermsBiggerThanInputSize();
}
Name = string.Format("Promedio móvil ({0})", movingAverageTerms);
//Call function to calculate the 'n' next periods
double[] full = MovingAverageHelper.Calculate(
inputValue.ToList(), movingAverageTerms, new List <double>()).ToArray <double>();
//Check if a value is less than 0
if (full.Where(f => f < 0).Count() > 0)
{
Name = "!!!" + Name;
}
return(ArrayBased.Split(full, 1));
}
//116
public static (double[], double[]) Calculate(double[] inputValue, int amountOfPeriodsToCalculate, int movingAverageTerms)
{
if (0 > movingAverageTerms)
{
throw new NegativeMovingAverageTerms();
}
if (inputValue.Length < movingAverageTerms)
{
return(new List <double>()
{
-1
}.ToArray(), new List <double>()
{
-1
}.ToArray()); // throw new MovingAverageTermsBiggerThanInputSize();
}
if (0 > amountOfPeriodsToCalculate)
{
throw new NegativePeriodsToCalculate();
}
Name = string.Format("Promedio móvil doble ({0})", movingAverageTerms);
//Calculate first moving average
double[] firstAverage = ArrayBased.Join(MovingAverage.Calculate(inputValue, movingAverageTerms));
//Calculate second moving average
double[] secondAverage = ArrayBased.Join(MovingAverage.Calculate(firstAverage, movingAverageTerms));
//Remove first movingAverageTerms - 1 values since they are not use
List <double> fixFirstAverage = firstAverage.ToList().Skip(movingAverageTerms - 1).ToList();
//Call function to calculate the 'n' next periods
double[] full = DoubleMovingAverageHelper.Calculate(
fixFirstAverage, secondAverage.ToList(), new List <double>(),
movingAverageTerms, 1, amountOfPeriodsToCalculate).ToArray <double>();
//Check if a value is less than 0
if (full.Where(f => f < 0).Count() > 0)
{
Name = "!!!" + Name;
}
return(ArrayBased.Split(full, amountOfPeriodsToCalculate));
}
public static (double[], double[]) Calculate(
double[] inputValue, int amountOfPeriodsToCalculate, decimal dataSmoothingFactor, decimal trendSmoothingFactor)
{
SmallestError = double.MaxValue;
double initalSmoothedValue = inputValue[0];
(List <double>, List <double>)auxValues = HoltHelper.CalculteSmoothedAndTrendValues(
inputValue.Select(d => (decimal)d).ToList(), dataSmoothingFactor, trendSmoothingFactor, new List <decimal>(), new List <decimal>());
//Calculate forecast for periods which already have real values
double[] full =
HoltHelper.Calculate(auxValues.Item1, auxValues.Item2, inputValue.Take(1).ToList()).ToArray <double>();
(double[], double[])calculated = ArrayBased.Split(full, amountOfPeriodsToCalculate);
double mad = MeanAbsoluteDeviation.Calculation(inputValue, calculated.Item1);
if (!double.IsNaN(mad) && mad < SmallestError)
{
SmallestError = mad;
BestDataSmoothing = (double)dataSmoothingFactor;
BestTrendSmoothing = (double)trendSmoothingFactor;
BestResult = calculated;
}
return(calculated);
}