TimeSeries趋势数据的重采样,聚合和插值

Question

在分析能源需求和消费数据时,我有问题重新采样和插值时间序列趋势数据.

数据集示例:

timestamp                value kWh
------------------       ---------
12/19/2011 5:43:21 PM    79178
12/19/2011 5:58:21 PM    79179.88
12/19/2011 6:13:21 PM    79182.13
12/19/2011 6:28:21 PM    79183.88
12/19/2011 6:43:21 PM    79185.63

根据这些观察结果,我希望根据一段时间对某些汇总值进行汇总,将频率设置为一个时间单位.

如同,小时间隔填补了缺失数据的任何空白

timestamp                value (approx)
------------------       ---------
12/19/2011 5:00:00 PM    79173
12/19/2011 6:00:00 PM    79179
12/19/2011 7:00:00 PM    79186

对于线性算法,似乎我会在时间上采用差异并将该值乘以该因子.

TimeSpan ts = current - previous;

Double factor = ts.TotalMinutes / period;

可以基于因子来计算值和时间戳.

有了这么多的可用信息,我不确定为什么很难找到最优雅的方法.

也许首先,是否有推荐的开源分析库？

对程序化方法的任何建议？理想情况下是C#,还是可能是SQL？

或者,我可以指出任何类似的问题(答案)？

Answer 1

我编写了一个 LINQ 函数来插值和规范化时间序列数据，以便可以聚合/合并它。

重采样函数如下。我在代码项目上写了一篇关于这项技术的短文。

// The function is an extension method, so it must be defined in a static class.
public static class ResampleExt
{
    // Resample an input time series and create a new time series between two 
    // particular dates sampled at a specified time interval.
    public static IEnumerable<OutputDataT> Resample<InputValueT, OutputDataT>(

        // Input time series to be resampled.
        this IEnumerable<InputValueT> source,

        // Start date of the new time series.
        DateTime startDate,

        // Date at which the new time series will have ended.
        DateTime endDate,

        // The time interval between samples.
        TimeSpan resampleInterval,

        // Function that selects a date/time value from an input data point.
        Func<InputValueT, DateTime> dateSelector,

        // Interpolation function that produces a new interpolated data point
        // at a particular time between two input data points.
        Func<DateTime, InputValueT, InputValueT, double, OutputDataT> interpolator
    )
    {
        // ... argument checking omitted ...

        //
        // Manually enumerate the input time series...
        // This is manual because the first data point must be treated specially.
        //
        var e = source.GetEnumerator();
        if (e.MoveNext())
        {
            // Initialize working date to the start date, this variable will be used to 
            // walk forward in time towards the end date.
            var workingDate = startDate;

            // Extract the first data point from the input time series.
            var firstDataPoint = e.Current;

            // Extract the first data point's date using the date selector.
            var firstDate = dateSelector(firstDataPoint);

            // Loop forward in time until we reach either the date of the first
            // data point or the end date, which ever comes first.
            while (workingDate < endDate && workingDate <= firstDate)
            {
                // Until we reach the date of the first data point,
                // use the interpolation function to generate an output
                // data point from the first data point.
                yield return interpolator(workingDate, firstDataPoint, firstDataPoint, 0);

                // Walk forward in time by the specified time period.
                workingDate += resampleInterval; 
            }

            //
            // Setup current data point... we will now loop over input data points and 
            // interpolate between the current and next data points.
            //
            var curDataPoint = firstDataPoint;
            var curDate = firstDate;

            //
            // After we have reached the first data point, loop over remaining input data points until
            // either the input data points have been exhausted or we have reached the end date.
            //
            while (workingDate < endDate && e.MoveNext())
            {
                // Extract the next data point from the input time series.
                var nextDataPoint = e.Current;

                // Extract the next data point's date using the data selector.
                var nextDate = dateSelector(nextDataPoint);

                // Calculate the time span between the dates of the current and next data points.
                var timeSpan = nextDate - firstDate;

                // Loop forward in time until wwe have moved beyond the date of the next data point.
                while (workingDate <= endDate && workingDate < nextDate)
                {
                    // The time span from the current date to the working date.
                    var curTimeSpan = workingDate - curDate; 

                    // The time between the dates as a percentage (a 0-1 value).
                    var timePct = curTimeSpan.TotalSeconds / timeSpan.TotalSeconds; 

                    // Interpolate an output data point at the particular time between 
                    // the current and next data points.
                    yield return interpolator(workingDate, curDataPoint, nextDataPoint, timePct);

                    // Walk forward in time by the specified time period.
                    workingDate += resampleInterval; 
                }

                // Swap the next data point into the current data point so we can move on and continue
                // the interpolation with each subsqeuent data point assuming the role of 
                // 'next data point' in the next iteration of this loop.
                curDataPoint = nextDataPoint;
                curDate = nextDate;
            }

            // Finally loop forward in time until we reach the end date.
            while (workingDate < endDate)
            {
                // Interpolate an output data point generated from the last data point.
                yield return interpolator(workingDate, curDataPoint, curDataPoint, 1);

                // Walk forward in time by the specified time period.
                workingDate += resampleInterval; 
            }
        }
    }
}

Answer 2

通过使用内部用于表示DateTimes的时间刻度,您可以获得最准确的值.由于这些时间刻度不会在午夜重新开始,因此您在日间边界不会出现问题.

// Sample times and full hour
DateTime lastSampleTimeBeforeFullHour = new DateTime(2011, 12, 19, 17, 58, 21);
DateTime firstSampleTimeAfterFullHour = new DateTime(2011, 12, 19, 18, 13, 21);
DateTime fullHour = new DateTime(2011, 12, 19, 18, 00, 00);

// Times as ticks (most accurate time unit)
long t0 = lastSampleTimeBeforeFullHour.Ticks;
long t1 = firstSampleTimeAfterFullHour.Ticks;
long tf = fullHour.Ticks;

// Energy samples
double e0 = 79179.88; // kWh before full hour
double e1 = 79182.13; // kWh after full hour
double ef; // interpolated energy at full hour

ef = e0 + (tf - t0) * (e1 - e0) / (t1 - t0); // ==> 79180.1275 kWh

公式说明
在几何图形中,类似的三角形是具有相同形状但大小不同的三角形.上述公式基于以下事实:一个三角形中任何两个边的比率对于相似三角形的相应边是相同的.

如果你有一个三角形ABC和一个类似的三角形abc,那么A : B = a : b.两个比率的相等性称为比例.

我们可以将这个比例规则应用于我们的问题:

(e1 – e0) / (t1 – t0) = (ef – e0) / (tf – t0)
--- large triangle --   --- small triangle --