Question

我有两个系列：range（20）和range（0，40，2）。我试图用pyflux.GARCH预测下一个值。我期望第一个系列的结果是：20 21 22 23 24 我期望第二个系列的结果是：39 41 43 45 47

对于第一个我得到：222.604657 233.956042 245.563616 256.949521 268.109078 对于第二个，我得到：889.877348 934.948259 980.351219 1025.914387 1071.470460

这些不是我期望的预测值。

有人可以解释结果吗？

代码：

var inputtedUID = "0";  // doesn't matter
db.foo.aggregate(
[
// This $match finds the docs with our input UID:
{$match: {"uid": inputtedUID }}

// ... and the $addFields/$filter will strip out those entries in contacts where contacts.uid does NOT exist.  We wish we could use {cond: {$zz.name: {$exists:true} }} but
// we cannot use $exists here so we need the convoluted $ifNull treatment.  Note we
// overwrite the original contacts with the filtered contacts:
,{$addFields: {contacts: {$filter: {
                input: "$contacts",
                as: "zz",
                cond: {$ne: [ {$ifNull:["$$zz.uid",null]}, null]}
            }}
    }}
,{$limit:1}  // just get 1 like findOne()
 ]);

show(c);

{
    "_id" : 0,
    "uid" : 0,
    "contacts" : [
        {
            "uid" : "buzz",
            "n" : 1
        },
        {
            "uid" : "dave",
            "n" : 2
        }
    ]
}

结果：

import pandas as pd
import pyflux as pf


df = pd.DataFrame(
    {
        'a': list(range(20)),
        'b': list(range(0, 40, 2))
    }
)

model = pf.GARCH(df, target='a', p=1, q=1)
model.fit()
predictions_a = model.predict(5)
print(predictions_a)

model = pf.GARCH(df, target='b', p=1, q=1)
model.fit()
predictions_b = model.predict(5)
print(predictions_b)

pyflux GARCH模型预测结果的含义是什么？

0 个答案: