fix next_lags

examples/anomaly_detection_pytorch.py

@@ -152,6 +152,7 @@ def main(args):
  distr = model.output_distribution(
  params, trailing_n=1, scale=scale
  )
+ scaled_past_target = inputs["past_target"] / scale
  batch_anomalies = []
  for i in tqdm(
  range(inputs["future_target"].shape[1]),
@@ -178,13 +179,16 @@ def main(args):
  )
  next_lags = lagged_sequence_values(
  model.lags_seq,
- inputs["past_target"] / scale,
+ scaled_past_target,
  target / scale,
  dim=-1,
  )
  rnn_input = torch.cat((next_lags, next_features), dim=-1)
-
  output, state = model.rnn(rnn_input, state)
+ scaled_past_target = torch.cat(
+ (scaled_past_target, target / scale), dim=1
+ )
+
  params = model.param_proj(output)
  distr = model.output_distribution(params, scale=scale)
  # stack the batch_anomalies along the prediction length dimension