Hello all!
I am following the tutorial about Time Series from the official documentation of TensorFlow (https://www.tensorflow.org/tutorials/structured_data/time_series 8), and I am facing difficulties caused by the occurrence of the error below (which also occurs in other code snippets that run later), when I apply my input data (time series), that has 1440 points, to the code, and I change the code to forecast 300 points in the future (OUT_STEPS = 300, label_width=OUT_STEPS,                           shift=OUT_STEPS, etc.), also adjusting the inputs to 300.
Here is one of the excerpts with which I have an error, and below, the output presented (error) from its execution:
Code executed:
history = compile_and_fit(lstm_model, wide_window)

IPython.display.clear_output()

multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val)
multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0)
multi_window.plot(feedback_model)

Output from execution:
/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:915: RuntimeWarning: divide by zero encountered in log10
  numdigits = int(np.log10(self.target)) + 1
---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-63-8a2e627c43f4> in <module>()
      2 
      3 IPython.display.clear_output()
----> 4 val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)
      5 performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)

4 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py in update(self, current, values, finalize)
    913 
    914       if self.target is not None:
--> 915         numdigits = int(np.log10(self.target)) + 1
    916         bar = ('%' + str(numdigits) + 'd/%d [') % (current, self.target)
    917         prog = float(current) / self.target

OverflowError: cannot convert float infinity to integer

I concluded that there is some dependency between the number of data entry and the number of forecast points, but not what it would be, as if I set the number of forecast points to 300 points in the example of the TensorFlow website, with number of input like 70091 (considering df = df[5::6]), this type of error that I mentioned does not occur, but if I select only 1440 points, the same error that occurs applying my data of 1440 points also occurs. If you want you can check/edit the example code from the TensorFlow website, in which I set the input number 1440 points, and made the necessary settings to predict 300 points, here on this Google Colab 3.
Could you help me with this please?
Thanks in advance.