我可能在这里有一些误解，所以如果有人可以帮我解决以下问题，我将非常感激：
我想训练一个HMM模型来预测时间序列的未来值（基本上与证券交易所预测相同的问题），所以我想出的是以下（使用hmm.learn库）：
进口报表：
__future__ import print_function
import datetime
import numpy as np
from matplotlib import cm, pyplot as plt
from matplotlib.dates import YearLocator, MonthLocator
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=DeprecationWarning)
from hmmlearn.hmm import GaussianHMM
import pandas as pd
预测功能：
def predictnext(model,data):
max_score=0
returnvalue = 0
for i in np.arange(-0.5,0.5,0.01):
X=np.concatenate((data[-2:],np.asarray([np.asarray([i])])))
s=np.exp(model.score(X))
print(str(i)+" "+str(np.exp(s)))
if(s>max_score):
max_score=s
returnvalue = i
return returnvalue
主要：
values=np.loadtxt("values.txt",usecols=(1,)) #1000 datapoints
n_obs=len(values)-1
n_feat=1
data = np.zeros((n_obs,n_feat))
for i in range(0,999):
data[i,0] = (values[i+1]-values[i])/(values[i])
values=data
mdl=GaussianHMM(n_components=3,covariance_type='diag',n_iter=100)
mdl=mdl.fit(values)
for i in range(0,10):
f=predictnext(mdl,values)
values=np.concatenate([values,np.asarray([np.asarray([f])])])
代码应该做什么：1）在一组数据点上安装HMM 2）对下一个最可能的值执行网格搜索 . 通过考虑序列的最后两点（在代码示例中，但并不重要）来做到这一点 . 3）将获得的值附加到数据集 . 4）重复
问题是用函数得分获得的概率总是最高的-0.01 . 在我看来，这个结果与我为函数提供的序列无关 .
现在有人在这里做错了吗？干杯
Edit 1: 添加样本数据（希望以这种方式出现）并更正导入语句
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