public ForecastingData CreateTimeFeatures(ForecastingData forecastData)
{
// Note: These properties could be set at the model level but to better illustrate the
// concepts of time series it was more clear to put them in a method with the other
// data time processing steps.
forecastData.Year = forecastData.Time.Year;
forecastData.Month = forecastData.Time.Month;
forecastData.WeekOfMonth = Convert.ToInt32(Math.Ceiling(forecastData.Time.Day / 7.0));
forecastData.WeekOfYear = Convert.ToInt32(Math.Ceiling(forecastData.Time.DayOfYear / 7.0));
// 4th Friday in November
forecastData.IsBlackFriday = forecastData.DatesInWeek.Any(date => date.Month == 11 &&
date.DayOfWeek == DayOfWeek.Friday &&
date.Day > 22 &&
date.Day < 29);
// 1st Monday in September
forecastData.IsUsLaborDay = forecastData.DatesInWeek.Any(date => date.Month == 9 &&
date.DayOfWeek == DayOfWeek.Monday &&
date.Day < 8);
// 25th of December
forecastData.IsChristmasDay = forecastData.DatesInWeek.Any(date => date.Month == 12 && date.Day == 25);
// 1st of January
forecastData.IsUsNewYearsDay = forecastData.DatesInWeek.Any(date => date.Month == 1 && date.Day == 1);
return(forecastData);
}
public List <ForecastingData> AddWeeksToPredict(List <ForecastingData> forecastData, int weeksToPredict, int storeID1, int itemID2)
{
var latestData = forecastData.OrderByDescending(data => data.Time).First();
var latestDate = latestData.Time;
var latestRdpi = latestData.RDPI;
for (int i = 0; i < weeksToPredict; i++)
{
latestDate = latestDate.AddDays(7);
var forcastingDataItem = new ForecastingData
{
ID1 = storeID1,
ID2 = itemID2,
Time = latestDate,
Value = 0,
RDPI = latestRdpi,
DatesInWeek = new List <DateTime>()
};
// Populate dates in week for new items.
for (var dt = forcastingDataItem.Time; dt <= forcastingDataItem.Time.AddDays(6); dt = dt.AddDays(1))
{
forcastingDataItem.DatesInWeek.Add(dt);
}
// Create Time Features for new week.
forcastingDataItem = CreateTimeFeatures(forcastingDataItem);
// Create Fourier Features for new week.
forcastingDataItem = CreateFourierFeatures(forcastingDataItem);
forecastData.Add(forcastingDataItem);
}
return(forecastData);
}
public static List <ForecastingData> GetForecastingDataFromLocal(string fileName)
{
string sourceFile = $"{Environment.CurrentDirectory}\\Datasets\\{fileName}.csv";
return(File.ReadAllLines(sourceFile)
.Skip(1)
.Select(line => ForecastingData.FromCsv(line))
.ToList());
}
public static List <ForecastingData> GetForecastingDataFromLocal(string fileName)
{
//set path based on OS
string sourceFile = RuntimeInformation.IsOSPlatform(OSPlatform.Windows) ?
$"{Environment.CurrentDirectory}\\Datasets\\{fileName}.csv"
: sourceFile = $"{Environment.CurrentDirectory}//Datasets//{fileName}.csv";
return(File.ReadAllLines(sourceFile)
.Skip(1)
.Select(line => ForecastingData.FromCsv(line))
.ToList());
}
public ForecastingData CreateFourierFeatures(ForecastingData forecastData)
{
// Set seasonality to 52 which is the number of weeks in a year.
var seasonality = 52;
forecastData.FreqCos1 = Math.Cos(forecastData.WeekOfYear * 2 * Math.PI * 1 / seasonality);
forecastData.FreqSin1 = Math.Sin(forecastData.WeekOfYear * 2 * Math.PI * 1 / seasonality);
forecastData.FreqCos2 = Math.Cos(forecastData.WeekOfYear * 2 * Math.PI * 2 / seasonality);
forecastData.FreqSin2 = Math.Sin(forecastData.WeekOfYear * 2 * Math.PI * 2 / seasonality);
forecastData.FreqCos3 = Math.Cos(forecastData.WeekOfYear * 2 * Math.PI * 3 / seasonality);
forecastData.FreqSin3 = Math.Sin(forecastData.WeekOfYear * 2 * Math.PI * 3 / seasonality);
forecastData.FreqCos4 = Math.Cos(forecastData.WeekOfYear * 2 * Math.PI * 4 / seasonality);
forecastData.FreqSin4 = Math.Sin(forecastData.WeekOfYear * 2 * Math.PI * 4 / seasonality);
return(forecastData);
}
