运行StatsForecast遇TypeError：预期dtype object却获numpy.dtype[float32]

问题 #

严格遵循《使用ARIMA和ETS进行预测》教程，在Anaconda的Jupyter Notebook中执行代码，运行到Y_hat_df = sf.forecast(horizon)时抛出错误：TypeError: expected dtype object, got 'numpy.dtype[float32]'。

完整代码 #

import numpy as np
import pandas as pd
from IPython.display import display, Markdown
import matplotlib.pyplot as plt
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, ETS, Naive #Imports the models you will use
from statsforecast.utils import AirPassengersDF

Y_df = AirPassengersDF
Y_df.head()

输出：

unique_id    ds          y
0  1.0          1949-01-31  112.0
1  1.0          1949-02-28  118.0
2  1.0          1949-03-31  132.0
3  1.0          1949-04-30  129.0
4  1.0          1949-05-31  121.0

Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 monthly observations for train
Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 monthly observations for test

season_length = 12 # Monthly data 
horizon = len(Y_test_df) # Predict the lenght of the test df

# Include the models you imported
models = [
    AutoARIMA(season_length=season_length),
    ETS(season_length=season_length),
    Naive()
]

# Instansiate the StatsForecast class as sf
sf = StatsForecast(
    df=Y_train_df,
    models=models,
    freq='M', 
    n_jobs=-1
)

# Forecast for the defined horizon
Y_hat_df = sf.forecast(horizon)

Y_hat_df.head()

报错堆栈 #

TypeError                                 Traceback (most recent call last)
<ipython-input-10-a9ee1bd8ce20> in <module>
     18 
     19 # Forecast for the defined horizon
---> 20 Y_hat_df = sf.forecast(horizon)
     21 
     22 Y_hat_df.head()

~\spyder\lib\site-packages\statsforecast\core.py in forecast(self, h, df, X_df, level, fitted, sort_df)
    668         X, level = self._parse_X_level(h=h, X=X_df, level=level)
    669         if self.n_jobs == 1:
---> 670             res_fcsts = self.ga.forecast(
    671                 models=self.models,
    672                 h=h,

~\spyder\lib\site-packages\statsforecast\core.py in forecast(self, models, h, fallback_model, fitted, X, level, verbose)
    197                         )
    198                     else:
---> 199                         raise error
    200                 cols_m = [
    201                     key

~\spyder\lib\site-packages\statsforecast\core.py in forecast(self, models, h, fallback_model, fitted, X, level, verbose)
    183                     kwargs["level"] = level
    184                 try:
---> 185                     res_i = model.forecast(
    186                         h=h, y=y_train, X=X_train, X_future=X_f, fitted=fitted, **kwargs
    187                     )

~\spyder\lib\site-packages\statsforecast\models.py in forecast(self, y, h, X, X_future, level, fitted)
    306         """
    307         with np.errstate(invalid="ignore"):
---> 308             mod = auto_arima_f(
    309                 x=y,
    310                 d=self.d,

~\spyder\lib\site-packages\statsforecast\arima.py in auto_arima_f(x, d, D, max_p, max_q, max_P, max_Q, max_order, max_d, max_D, start_p, start_q, start_P, start_Q, stationary, seasonal, ic, stepwise, nmodels, trace, approximation, method, truncate, xreg, test, test_kwargs, seasonal_test, seasonal_test_kwargs, allowdrift, allowmean, blambda, biasadj, parallel, num_cores, period)
   1785         D = 0
   1786     elif D is None:
---> 1787         D = nsdiffs(
   1788             xx, period=m, test=seasonal_test, max_D=max_D, **seasonal_test_kwargs
   1789         )

~\spyder\lib\site-packages\statsforecast\arima.py in nsdiffs(x, test, alpha, period, max_D, **kwargs)
   1608     while dodiff and D < max_D:
   1609         D += 1
---> 1610         x = diff(x, period, 1)
   1611         if is_constant(x):
   1612             return D

~\spyder\lib\site-packages\statsforecast\arima.py in diff(x, lag, differences)
    583 def diff(x, lag, differences):
    584     if x.ndim == 1:
---> 585         y = diff1d(x, lag, differences)
    586         nan_mask = np.isnan(y)
    587     elif x.ndim == 2:

TypeError: expected dtype object, got 'numpy.dtype[float32]'

预期输出 #

unique_id  ds           AutoARIMA   ETS         Naive   
1.0        1960-01-31   424.160156  406.651276  405.0
1.0        1960-02-29   407.081696  401.732910  405.0
1.0        1960-03-31   470.860535  456.289642  405.0
1.0        1960-04-30   460.913605  440.870514  405.0
1.0        1960-05-31   484.900879  440.333923  405.0

错误原因分析 #

报错根源是AirPassengersDF中的y列数据类型为float32，而statsforecast库底层ARIMA实现依赖的diff1d函数仅支持float64（numpy默认浮点类型），不兼容float32类型，导致类型匹配错误。

解决方案 #

将数据集的y列转换为float64类型即可解决问题，修改加载数据集的代码部分：

Y_df = AirPassengersDF.copy()
Y_df['y'] = Y_df['y'].astype(np.float64)

完整修改后的代码片段：

import numpy as np
import pandas as pd
from IPython.display import display, Markdown
import matplotlib.pyplot as plt
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, ETS, Naive
from statsforecast.utils import AirPassengersDF

# 加载数据集并转换y列数据类型
Y_df = AirPassengersDF.copy()
Y_df['y'] = Y_df['y'].astype(np.float64)
Y_df.head()

之后按原流程执行后续代码即可得到预期输出。

内容的提问来源于stack exchange，提问作者user11222532