MongoDBの集計フレームワークで移動平均？

aggフレームワークに$mapが追加されましたおよび$reduce および$range 組み込みなので、配列処理ははるかに簡単です。以下は、いくつかの述語でフィルタリングしたいデータセットの移動平均を計算する例です。基本的な設定は、各ドキュメントにフィルタリング可能な基準と値が含まれていることです。例：

{sym: "A", d: ISODate("2018-01-01"), val: 10}
{sym: "A", d: ISODate("2018-01-02"), val: 30}

ここにあります：

// This controls the number of observations in the moving average:
days = 4;

c=db.foo.aggregate([

// Filter down to what you want.  This can be anything or nothing at all.
{$match: {"sym": "S1"}}

// Ensure dates are going earliest to latest:
,{$sort: {d:1}}

// Turn docs into a single doc with a big vector of observations, e.g.
//     {sym: "A", d: d1, val: 10}
//     {sym: "A", d: d2, val: 11}
//     {sym: "A", d: d3, val: 13}
// becomes
//     {_id: "A", prx: [ {v:10,d:d1}, {v:11,d:d2},  {v:13,d:d3} ] }
//
// This will set us up to take advantage of array processing functions!
,{$group: {_id: "$sym", prx: {$push: {v:"$val",d:"$date"}} }}

// Nice additional info.  Note use of dot notation on array to get
// just scalar date at elem 0, not the object {v:val,d:date}:
,{$addFields: {numDays: days, startDate: {$arrayElemAt: [ "$prx.d", 0 ]}} }

// The Juice!  Assume we have a variable "days" which is the desired number
// of days of moving average.
// The complex expression below does this in python pseudocode:
//
// for z in range(0, size of value vector - # of days in moving avg):
//    seg = vector[n:n+days]
//    values = seg.v
//    dates = seg.d
//    for v in seg:
//        tot += v
//    avg = tot/len(seg)
// 
// Note that it is possible to overrun the segment at the end of the "walk"
// along the vector, i.e. not enough date-values.  So we only run the
// vector to (len(vector) - (days-1).
// Also, for extra info, we also add the number of days *actually* used in the
// calculation AND the as-of date which is the tail date of the segment!
//
// Again we take advantage of dot notation to turn the vector of
// object {v:val, d:date} into two vectors of simple scalars [v1,v2,...]
// and [d1,d2,...] with $prx.v and $prx.d
//
,{$addFields: {"prx": {$map: {
    input: {$range:[0,{$subtract:[{$size:"$prx"}, (days-1)]}]} ,
    as: "z",
    in: {
       avg: {$avg: {$slice: [ "$prx.v", "$$z", days ] } },
       d: {$arrayElemAt: [ "$prx.d", {$add: ["$$z", (days-1)] } ]}
        }
        }}
    }}

            ]);

これにより、次の出力が生成される可能性があります。

{
    "_id" : "S1",
    "prx" : [
        {
            "avg" : 11.738793632512115,
            "d" : ISODate("2018-09-05T16:10:30.259Z")
        },
        {
            "avg" : 12.420766702631376,
            "d" : ISODate("2018-09-06T16:10:30.259Z")
        },
        ...

    ],
    "numDays" : 4,
    "startDate" : ISODate("2018-09-02T16:10:30.259Z")
}