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fixed batch_forecast #2

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fixed batch_forecast #2

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16 changes: 14 additions & 2 deletions ts_benchmark/baselines/deep_forecasting_model_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -715,6 +715,7 @@ def forecast(
output = output[:, -config.horizon:, :series_dim]
break


column_num = output.shape[-1]
temp = output.cpu().numpy().reshape(-1, column_num)[-config.horizon:]

Expand Down Expand Up @@ -873,7 +874,7 @@ def _perform_rolling_predictions(
"""
rolling_time = 0
input_np, target_np, input_mark_np, target_mark_np = self._get_rolling_data(
input_np, None, all_mark, rolling_time
input_np, None, None, all_mark, rolling_time
)
if exog_future is not None:
rolling_time_sum = horizon // self.config.horizon + 1
Expand Down Expand Up @@ -933,12 +934,13 @@ def _perform_rolling_predictions(
break
rolling_time += 1
output = output.cpu().numpy()[:, -self.config.horizon :, :]
# 这里的_get_rolling_data函数需要修改：
(
input_np,
target_np,
input_mark_np,
target_mark_np,
) = self._get_rolling_data(input_np, output, all_mark, rolling_time)
) = self._get_rolling_data(input_np, output, exog_future, all_mark, rolling_time)

answers = np.concatenate(answers, axis=1)
return answers[:, -horizon:, :]
Expand All @@ -947,6 +949,7 @@ def _get_rolling_data(
self,
input_np: np.ndarray,
output: Optional[np.ndarray],
exog_future: Optional[torch.Tensor],
all_mark: np.ndarray,
rolling_time: int,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
Expand All @@ -960,6 +963,15 @@ def _get_rolling_data(
:return: Updated input data, target data, input marks, and target marks for rolling prediction.
"""
if rolling_time > 0:
if exog_future!= None:
exog_output = exog_future[
:,
rolling_time
* self.config.horizon : (rolling_time + 1)
* self.config.horizon,
:,
].cpu().numpy()
output = np.concatenate((output, exog_output), axis=2)
input_np = np.concatenate((input_np, output), axis=1)
input_np = input_np[:, -self.config.seq_len :, :]
target_np = np.zeros(
Expand Down