private void RemoveNonstationarity(ref List <double> series, ref int startIndex, out int regularDifferencingLevel)
{
int dataSize = series.Count - startIndex;
List <double> listAutocorrelation = new List <double>();
List <double> listConfidenceLimit = new List <double>();
regularDifferencingLevel = 0;
while (true)
{
listAutocorrelation.Clear();
listConfidenceLimit.Clear();
dataSize = series.Count - startIndex;
Statistic.ComputeAutocorrelation(series, startIndex, out listAutocorrelation);
Statistic.ComputeConfidenceLimit(listAutocorrelation, dataSize, out listConfidenceLimit);
//DrawSeriesData(series, startIndex);
//DrawAutocorrelation(listAutocorrelation, listConfidenceLimit);
DecayPartern decayPartern = ComputeDecayPartern(listAutocorrelation, listConfidenceLimit);
if (decayPartern != DecayPartern.SLOW_DECAY)
{
break;
}
startIndex += 1;
regularDifferencingLevel++;
for (int j = series.Count - 1; j >= startIndex; j--)
{
series[j] = series[j] - series[j - 1];
}
}
}
private void RemoveSeasonality(ref List <double> processSeries, ref int startIndex, out int seasonPartern, out int seasonDifferencingLevel)
{
seasonPartern = 0;
seasonDifferencingLevel = 0;
List <double> listAutocorrelation = new List <double>();
List <double> listConfidenceLimit = new List <double>();
List <int> levelLocation = new List <int>();
while (true)
{
listAutocorrelation.Clear();
Statistic.ComputeAutocorrelation(processSeries, startIndex, out listAutocorrelation);
Statistic.ComputeConfidenceLimit(listAutocorrelation, processSeries.Count - startIndex, out listConfidenceLimit);
//DrawSeriesData(series, startIndex);
//DrawAutocorrelation(listAutocorrelation, listConfidenceLimit);
int newSeasonPartern;
EstimateSeasonPartern(listAutocorrelation, listConfidenceLimit, out newSeasonPartern);
if (newSeasonPartern == 0 || (seasonPartern != 0 && newSeasonPartern != seasonPartern))
{
break;
}
seasonPartern = newSeasonPartern;
seasonDifferencingLevel++;
startIndex += seasonPartern;
for (int j = processSeries.Count - 1; j >= startIndex; j--)
{
processSeries[j] = processSeries[j] - processSeries[j - seasonPartern];
}
}
}
public void DrawAutocorrelation()
{
List <double> listAutocorrelation;
List <double> listConfidenceLimit;
Statistic.ComputeAutocorrelation(_processARIMASeries, _startIndex, out listAutocorrelation);
Statistic.ComputeConfidenceLimit(listAutocorrelation, _processARIMASeries.Count, out listConfidenceLimit);
Statistic.DrawAutocorrelation(listAutocorrelation, listConfidenceLimit);
}
private void IdentifyARIMAModel(List <double> series, int startIndex, int seasonPartern, out int pRegular, out int qRegular, out int pSeason, out int qSeason)
{
pRegular = qRegular = pSeason = qSeason = 0;
List <double> listAutocorrelation = new List <double>();
List <double> listConfidenceLimit = new List <double>();
List <double> listPartialAutocorrelation = new List <double>();
List <double> listSeasonAutocorrelation = new List <double>();
List <double> listSeasonConfidenceLimit = new List <double>();
List <double> listSeasonPartialCorrelation = new List <double>();
List <double> listRegularAutocorrelation = new List <double>();
List <double> listRegularConfidenceLimit = new List <double>();
List <double> listRegularPartialCorrelation = new List <double>();
Statistic.ComputeAutocorrelation(series, startIndex, out listAutocorrelation);
Statistic.ComputePartialAutocorrelation(listAutocorrelation, out listPartialAutocorrelation);
Statistic.ComputeConfidenceLimit(listAutocorrelation, series.Count - startIndex, out listConfidenceLimit);
double confidenceLimit = 1.96 / Math.Sqrt(series.Count - startIndex);
int regularAutocorrelationLengh = seasonPartern;
if (seasonPartern == 0)
{
regularAutocorrelationLengh = listAutocorrelation.Count;
}
for (int i = 0; i < regularAutocorrelationLengh; i++)
{
listRegularAutocorrelation.Add(listAutocorrelation[i]);
listRegularConfidenceLimit.Add(listConfidenceLimit[i]);
listRegularPartialCorrelation.Add(listPartialAutocorrelation[i]);
}
//Algorithm.DrawAutocorrelation(listRegularAutocorrelation, listRegularConfidenceLimit);
//Algorithm.DrawPartialAutocorrelation(listRegularPartialCorrelation, confidenceLimit);
DecayPartern decayACF = ComputeDecayPartern(listRegularAutocorrelation, listRegularConfidenceLimit);
DecayPartern decayPACF = ComputeDecayPartern(listRegularPartialCorrelation, confidenceLimit);
if (decayACF == DecayPartern.ABRUPT_DECAY)
{
GetLastSignificant(listRegularAutocorrelation, listRegularConfidenceLimit, out qRegular);
}
if (decayPACF == DecayPartern.ABRUPT_DECAY)
{
GetLastSignificant(listRegularPartialCorrelation, confidenceLimit, out pRegular);
}
if (decayACF != DecayPartern.ABRUPT_DECAY && decayPACF != DecayPartern.ABRUPT_DECAY)
{
pRegular = qRegular = 2;
}
pRegular = Math.Min(pRegular, Configuration.MAX_REGULAR_ARMA_ORDER);
qRegular = Math.Min(qRegular, Configuration.MAX_REGULAR_ARMA_ORDER);
if (seasonPartern == 0)
{
return;
}
for (int i = 0; i < Math.Floor(1.0 * listAutocorrelation.Count / seasonPartern); i++)
{
listSeasonAutocorrelation.Add(listAutocorrelation[i * seasonPartern]);
listSeasonConfidenceLimit.Add(listConfidenceLimit[i * seasonPartern]);
listSeasonPartialCorrelation.Add(listPartialAutocorrelation[i * seasonPartern]);
}
//Algorithm.DrawAutocorrelation(listSeasonAutocorrelation, listSeasonConfidenceLimit);
//Algorithm.DrawPartialAutocorrelation(listSeasonPartialCorrelation, confidenceLimit);
DecayPartern decaySeasonACF = ComputeDecayPartern(listSeasonAutocorrelation, listSeasonConfidenceLimit);
DecayPartern decaySeasonPACF = ComputeDecayPartern(listSeasonPartialCorrelation, confidenceLimit);
if (decaySeasonACF == DecayPartern.ABRUPT_DECAY)
{
GetLastSignificant(listSeasonAutocorrelation, listSeasonConfidenceLimit, out qSeason);
}
if (decaySeasonPACF == DecayPartern.ABRUPT_DECAY)
{
GetLastSignificant(listSeasonPartialCorrelation, confidenceLimit, out pSeason);
}
if (decaySeasonACF != DecayPartern.ABRUPT_DECAY && decaySeasonPACF != DecayPartern.ABRUPT_DECAY)
{
pSeason = qSeason = 1;
}
pSeason = Math.Min(pSeason, Configuration.MAX_SEASON_ARMA_ORDER);
qSeason = Math.Min(qSeason, Configuration.MAX_SEASON_ARMA_ORDER);
}