# Error when running Probabilistic Time Series Forecasting **URL:** \ **Category:** Beginners\ **Created:** [May 1, 2023, 3:30am UTC](https://discuss.huggingface.co/t/error-when-running-probabilistic-time-series-forecasting/38239 "2023-05-01T03:30:32Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Zhongxian](https://avatars.discourse-cdn.com/v4/letter/z/71e660/32.png) [@Zhongxian](https://discuss.huggingface.co/u/Zhongxian)\ **Post date:** [May 1, 2023, 3:30am UTC](https://discuss.huggingface.co/t/error-when-running-probabilistic-time-series-forecasting/38239/1 "2023-05-01T03:30:32Z")
# Probabilistic Time Series Forecasting > **[Google Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb)** The following code gave me an error ```auto # perform forward pass outputs = model( past_values=batch["past_values"], past_time_features=batch["past_time_features"], past_observed_mask=batch["past_observed_mask"], static_categorical_features=batch["static_categorical_features"] if config.num_static_categorical_features > 0 else None, static_real_features=batch["static_real_features"] if config.num_static_real_features > 0 else None, future_values=batch["future_values"], future_time_features=batch["future_time_features"], future_observed_mask=batch["future_observed_mask"], output_hidden_states=True, ) ===================================================== --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[40], line 2 1 # perform forward pass ----> 2 outputs = model( 3 past_values=batch["past_values"], 4 past_time_features=batch["past_time_features"], 5 past_observed_mask=batch["past_observed_mask"], 6 static_categorical_features=batch["static_categorical_features"] 7 if config.num_static_categorical_features > 0 8 else None, 9 static_real_features=batch["static_real_features"] 10 if config.num_static_real_features > 0 11 else None, 12 future_values=batch["future_values"], 13 future_time_features=batch["future_time_features"], 14 future_observed_mask=batch["future_observed_mask"], 15 output_hidden_states=True, 16 ) File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs) 1126 # If we don't have any hooks, we want to skip the rest of the logic in 1127 # this function, and just call forward. 1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1129 or _global_forward_hooks or _global_forward_pre_hooks): -> 1130 return forward_call(*input, **kwargs) 1131 # Do not call functions when jit is used 1132 full_backward_hooks, non_full_backward_hooks = [], [] File ~\anaconda3\lib\site-packages\transformers\models\time_series_transformer\modeling_time_series_transformer.py:1813, in TimeSeriesTransformerForPrediction.forward(self, past_values, past_time_features, past_observed_mask, static_categorical_features, static_real_features, future_values, future_time_features, future_observed_mask, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, output_hidden_states, output_attentions, use_cache, return_dict) 1810 if future_values is not None: 1811 use_cache = False -> 1813 outputs = self.model( 1814 past_values=past_values, 1815 past_time_features=past_time_features, 1816 past_observed_mask=past_observed_mask, 1817 static_categorical_features=static_categorical_features, 1818 static_real_features=static_real_features, 1819 future_values=future_values, 1820 future_time_features=future_time_features, 1821 decoder_attention_mask=decoder_attention_mask, 1822 head_mask=head_mask, 1823 decoder_head_mask=decoder_head_mask, 1824 cross_attn_head_mask=cross_attn_head_mask, 1825 encoder_outputs=encoder_outputs, 1826 past_key_values=past_key_values, 1827 output_hidden_states=output_hidden_states, 1828 output_attentions=output_attentions, 1829 use_cache=use_cache, 1830 return_dict=return_dict, 1831 ) 1833 prediction_loss = None 1834 params = None File ~\anaconda3\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs) 1126 # If we don't have any hooks, we want to skip the rest of the logic in 1127 # this function, and just call forward. 1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks 1129 or _global_forward_hooks or _global_forward_pre_hooks): -> 1130 return forward_call(*input, **kwargs) 1131 # Do not call functions when jit is used 1132 full_backward_hooks, non_full_backward_hooks = [], [] File ~\anaconda3\lib\site-packages\transformers\models\time_series_transformer\modeling_time_series_transformer.py:1626, in TimeSeriesTransformerModel.forward(self, past_values, past_time_features, past_observed_mask, static_categorical_features, static_real_features, future_values, future_time_features, decoder_attention_mask, head_mask, decoder_head_mask, cross_attn_head_mask, encoder_outputs, past_key_values, output_hidden_states, output_attentions, use_cache, return_dict) 1623 use_cache = use_cache if use_cache is not None else self.config.use_cache 1624 return_dict = return_dict if return_dict is not None else self.config.use_return_dict -> 1626 transformer_inputs, scale, static_feat = self.create_network_inputs( 1627 past_values=past_values, 1628 past_time_features=past_time_features, 1629 past_observed_mask=past_observed_mask, 1630 static_categorical_features=static_categorical_features, 1631 static_real_features=static_real_features, 1632 future_values=future_values, 1633 future_time_features=future_time_features, 1634 ) 1636 if encoder_outputs is None: 1637 enc_input = transformer_inputs[:, : self.config.context_length, ...] File ~\anaconda3\lib\site-packages\transformers\models\time_series_transformer\modeling_time_series_transformer.py:1535, in TimeSeriesTransformerModel.create_network_inputs(self, past_values, past_time_features, static_categorical_features, static_real_features, past_observed_mask, future_values, future_time_features) 1533 # static features 1534 log_scale = scale.log() if self.config.input_size == 1 else scale.squeeze(1).log() -> 1535 static_feat = torch.cat((embedded_cat, static_real_features, log_scale), dim=1) 1536 expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1) 1538 # all features TypeError: expected Tensor as element 1 in argument 0, but got NoneType My Anaconda has Python version :slight_smile: !python --version Python 3.10.9 ​ ```