Update README.md

README.md

@@ -26,11 +26,13 @@ model = AutoModelForCausalLM.from_pretrained('thuml/timer-base', trust_remote_co
 
 # prepare input
 batch_size, lookback_length = 1, 2880
-seqs = torch.randn(batch_size, lookback_length) 
-mean, std = seqs.mean(dim=-1, keepdim=True), seqs.std(dim=-1, keepdim=True) # normalize the input to mitigate different scale
+seqs = torch.randn(batch_size, lookback_length)
+
+# normalize the input to mitigate different scale
+mean, std = seqs.mean(dim=-1, keepdim=True), seqs.std(dim=-1, keepdim=True) 
 normed_seqs = (seqs - mean) / std
 
-# forecast
+# generate forecast
 prediction_length = 96
 normed_output = model.generate(normed_seqs, max_new_tokens=prediction_length)[:, -prediction_length:]
 output = std * normed_output + mean # rescale the output to the original scale