Data Analysis Mathematics Linear Algebra Statistics
New Version 7.0!  QuickStart Samples

# Simple Time Series QuickStart Sample (C#)

Illustrates how to perform simple operations on time series data using classes in the Extreme.Statistics.TimeSeriesAnalysis namespace in C#.

```using System;
using System.Collections.Generic;
using System.Data;
using System.Data.OleDb;
using System.Linq;

using Extreme.DataAnalysis;
using Extreme.Mathematics;
using Extreme.Statistics;

namespace Extreme.Numerics.QuickStart.CSharp
{
/// <summary>
/// Illustrates the use of the TimeSeriesCollection class to represent
/// and manipulate time series data.
/// </summary>
class SimpleTimeSeries
{
/// <summary>
/// The main entry point for the application.
/// </summary>
static void Main(string[] args)
{
// Time series collections can be created in a variety of ways.
// Here we use an ADO.NET data table:
var timeSeries = DataFrame.FromDataTable<DateTime>(seriesTable, "Date");

// The RowCount property returns the number of
// observations:
Console.WriteLine("# observations: {0}", timeSeries.RowCount);

//
// Accessing variables
//

// Variables are accessed by name or numeric index.
// They need to be cast to the appropriate specialized
// type using the As() method:
var close = timeSeries["Close"].As<double>();
Console.WriteLine("Average close price: \${0:F2}", close.Mean());

// Variables can also be accessed by numeric index:
Console.WriteLine("3rd variable: {0}", timeSeries.Name);

// The GetRows method returns the data from the specified range.
DateTime y2004 = new DateTime(2004, 1, 1);
DateTime y2005 = new DateTime(2005, 1, 1);
var series2004 = timeSeries.GetRows(y2004, y2005);
Console.WriteLine("Opening price on the first trading day of 2004: {0}",
series2004["Open"].GetValue(0));

//
// Transforming the Frequency
//

// The first step is to define the aggregator function
// for each variable. This function specifies how each
// observation in the new time series is calculated
// from the observations in the original series.

// The Aggregators class has a number of
// pre-defined aggregator functions.

// We create a dictionary that maps column names
// to aggregators:
var aggregators = new Dictionary<string, AggregatorGroup>()
{
{ "Open", Aggregators.First },
{ "Close", Aggregators.Last },
{ "High", Aggregators.Max },
{ "Low", Aggregators.Min },
{ "Volume", Aggregators.Sum}
};

// We can then resample the data frame in accordance with
// a recurrence pattern we specify, in this case monthly:
var monthlySeries = timeSeries.Resample(Recurrence.Monthly, aggregators);

// We can specify a subset of the series by selecting it
// from the data frame first:
monthlySeries = timeSeries.GetRows(y2004, y2005)
.Resample(Recurrence.Monthly, aggregators);

// We can now print the results:
Console.WriteLine("Monthly statistics for Microsoft Corp. (MSFT)");
Console.WriteLine(monthlySeries.ToString());

Console.Write("Press any key to exit.");
}

{
string filename = @"..\..\..\..\Data\MicrosoftStock.xls";
string connectionString = "Provider=Microsoft.Jet.OLEDB.4.0;Data Source="+filename+";Extended Properties=\"Excel 8.0;HDR=Yes;IMEX=1\"";
OleDbConnection cnn = null;
DataSet ds = new DataSet();
try
{
cnn = new OleDbConnection(connectionString);
cnn.Open();
}
catch (OleDbException ex)
{
Console.WriteLine(ex.InnerException);
}
finally
{
if (cnn != null)
cnn.Close();
}
return ds.Tables;
}
}
}```