Update readme

README.md

@@ -1,13 +1,25 @@
-# Forecastable
-Code for "Anomalies in metro passenger demand are predictable -- causal inference with transformer"
+# Abnormal metro passenger demand is predictable
+Code for "Abnormal metro passenger demand is predictable from alighting and boarding correlation"
 The structure of the code is as follows:
 ```
-├── models             <- Implementation of different models, including the proposed, PatchTST, Dlinear, LSTM, etc.>
+├── models             <- Implementation of different models, including the proposed ABtransformer, Nlinear, and DeepAR.>
 ├── exps               <- Scripts to run experiments for different models. ->
      ├── ABtranformer  <- Experiments for the proposed ABtranformer. ->
-     ├── PatchTST      <- Experiments for PatchTST. ->
-     ├── Dlinear       <- Experiments for Dlinear. ->
-     ├── LSTM          <- Experiments for LSTM. ->
+     ├── Nlinear       <- Experiments for Nlinear. ->
+     ├── DeepAR        <- Experiments for DeepAR. ->
 ├── datasets           <- Scripts to prepare datasets for training and testing for different models. ->
 ├── utilites           <- Utilities like loss functions, learning rate schedulers, etc. ->
-```
+├── data               <- Data for the experiments. -> 
+```
+
+The idea is to use attention to model long-range correlations between alighting and boarding flow in metro stations.
+The model is named Alight-boarding Transformer (ABtransformer).
+ABTrasnformer can predict abnormal passenger boarding demand with a long lead time.
+
+![Model structure](figs/model_structure.png)
+
+
+The model is also interpretable, the boarding demand forecast at the checked location exhibits significant attention to periods
+with abnormal alighting demand, indicating the parts of the input sequence that contribute to the forecast at the checked
+location.
+![Interpretability](figs/interpret.png)