我正在尝试分析星星的数据。我有明星的时间序列,我想预测他们属于哪个班级(4种不同类型)。 我有那些明星的时间序列,我想通过去季节化,频率分析和其他可能相关的研究来分析这些时间序列。
对象time_series是一个熊猫DataFrame,包括10列:time_points_b,light_points_b(b代表蓝色)等......
我首先要研究蓝灯时间序列。
import statsmodels.api as sm;
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.mpl_style = 'default'
%matplotlib inline
def star_key(slab_id, star_id_b):
return str(slab_id) + '_' + str(star_id_b)
raw_time_series = pd.read_csv("data/public/train_varlength_features.csv.gz", index_col=0, compression='gzip')
time_series = raw_time_series.applymap(csv_array_to_float)
time_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['time_points_b'])
light_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['light_points_b'])
error_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['error_points_b'])
light_data = pd.DataFrame({'time':time_points[:], 'light':light_points[:]})
residuals = sm.tsa.seasonal_decompose(light_data);
light_plt = residuals.plot()
light_plt.set_size_inches(10, 5)
light_plt.tight_layout()
当我应用seasonal_decompose方法时,此代码给出了属性错误: AttributeError:'Int64Index'对象没有属性'inferred_freq'