熊猫回归

Question

我有两个单独的数据库 - 温度分贝与小时数据和房屋分贝与逐分钟数据为hvac使用。我试图将hvac数据绘制为一周，一个月和一年的温度系列，但由于增量与温度db不匹配，所以我遇到了麻烦。我尝试过使最小二乘法适合，但是a）不知道如何在熊猫中做一个，以及b）在一两天后变得非常不准确。有什么建议么？

Answer 1

pandastimeseries是完美的这个应用程序。你可以合并一系列不同的采样频率，并且pandas将完美对齐它们。然后，您可以对数据进行降采样并进行预回归，即使用statsmodels。一个模拟的例子：

In [288]: 

idx1=pd.date_range('2001/01/01', periods=10, freq='D') 
idx2=pd.date_range('2001/01/01', periods=500, freq='H') 
df1 =pd.DataFrame(np.random.random(10), columns=['val1']) 
df2 =pd.DataFrame(np.random.random(500), columns=['val2']) 
df1.index=idx1 
df2.index=idx2 
In [291]: 

df3=pd.merge(df1, df2, left_index=True, right_index=True, how='inner') 
df4=df3.resample(rule='D') 
In [292]: 

print df4 
       val1  val2 
2001-01-01 0.399901 0.244800 
2001-01-02 0.014448 0.423780 
2001-01-03 0.811747 0.070047 
2001-01-04 0.595556 0.679096 
2001-01-05 0.218412 0.116764 
2001-01-06 0.961310 0.040317 
2001-01-07 0.058964 0.606843 
2001-01-08 0.075129 0.407842 
2001-01-09 0.833003 0.751287 
2001-01-10 0.070072 0.559986 

[10 rows x 2 columns] 
In [294]: 

import statsmodels.formula.api as smf 
mod = smf.ols(formula='val1 ~ val2', data=df4) 
res = mod.fit() 
print res.summary() 
          OLS Regression Results        
============================================================================== 
Dep. Variable:     val1 R-squared:      0.061 
Model:       OLS Adj. R-squared:     -0.056 
Method:     Least Squares F-statistic:     0.5231 
Date:    Fri, 27 Jun 2014 Prob (F-statistic):    0.490 
Time:      10:46:34 Log-Likelihood:    -3.3643 
No. Observations:     10 AIC:        10.73 
Df Residuals:      8 BIC:        11.33 
Df Model:       1           
============================================================================== 
       coef std err   t  P>|t|  [95.0% Conf. Int.] 
------------------------------------------------------------------------------ 
Intercept  0.5405  0.224  2.417  0.042   0.025  1.056 
val2   -0.3502  0.484  -0.723  0.490  -1.467  0.766 
============================================================================== 
Omnibus:      3.509 Durbin-Watson:     2.927 
Prob(Omnibus):     0.173 Jarque-Bera (JB):    1.232 
Skew:       0.399 Prob(JB):      0.540 
Kurtosis:      1.477 Cond. No.       4.69 
==============================================================================