无法使用 ARIMA 预测下一个值：输入包含 NaN、无穷大或对于 dtype('float64') 来说太大的值

我有以下代码片段：

import pmdarima as pmd

ts = [-3, 86, 5, 52, 68, 14, 20, 12, 22, -4, -2]
model = pmd.auto_arima(ts, start_p=1, start_q=1, test="adf")
est = model.predict(1)[0]

它在堆栈跟踪的最后一行失败：

Traceback (most recent call last):
  File "/Users/josh/Code/nfl-scraper/score.py", line 117, in <module>
    do_arima(info, data)
  File "/Users/josh/Code/nfl-scraper/score.py", line 98, in do_arima
    est = model.predict(1)[0]
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pmdarima/arima/arima.py", line 676, in predict
    f, conf_int = _seasonal_prediction_with_confidence(
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/pmdarima/arima/arima.py", line 88, in _seasonal_prediction_with_confidence
    check_array(conf_int, copy=False, dtype=None)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
    return f(*args, **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py", line 720, in check_array
    _assert_all_finite(array,
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py", line 103, in _assert_all_finite
    raise ValueError(
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

由于中没有 NaN 或无限值ts，我不确定这是什么auto_arima函数正在返回或者为什么它返回包含 NaN/inf 的内容，我不确定如何调试它。任何帮助，将不胜感激。

我发现了一些事情：

• a NaN导致了这个问题——我打印出了引发这个错误的东西的值
• 当我有auto_arima函数打印它在做什么（通过设置 kwargtrace=True），看起来它正在工作：

Performing stepwise search to minimize aic
 ARIMA(1,2,1)(0,0,0)[0]             : AIC=inf, Time=0.04 sec
 ARIMA(0,2,0)(0,0,0)[0]             : AIC=106.377, Time=0.01 sec
 ARIMA(1,2,0)(0,0,0)[0]             : AIC=102.327, Time=0.01 sec
 ARIMA(0,2,1)(0,0,0)[0]             : AIC=inf, Time=0.02 sec
 ARIMA(2,2,0)(0,0,0)[0]             : AIC=98.349, Time=0.03 sec
 ARIMA(3,2,0)(0,0,0)[0]             : AIC=99.145, Time=0.03 sec
 ARIMA(2,2,1)(0,0,0)[0]             : AIC=98.258, Time=0.03 sec
 ARIMA(3,2,1)(0,0,0)[0]             : AIC=100.256, Time=0.04 sec
 ARIMA(2,2,2)(0,0,0)[0]             : AIC=inf, Time=0.15 sec
 ARIMA(1,2,2)(0,0,0)[0]             : AIC=inf, Time=0.10 sec
 ARIMA(3,2,2)(0,0,0)[0]             : AIC=12.000, Time=0.05 sec
 ARIMA(3,2,3)(0,0,0)[0]             : AIC=inf, Time=0.17 sec
 ARIMA(2,2,3)(0,0,0)[0]             : AIC=inf, Time=0.19 sec
 ARIMA(3,2,2)(0,0,0)[0] intercept   : AIC=14.000, Time=0.07 sec

Best model:  ARIMA(3,2,2)(0,0,0)[0]          
Total fit time: 0.952 seconds