Some options!

.gitignore

@@ -3,9 +3,16 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+test_results/test_iTransformer_custom_MS_ft15_sl1_ll3_pl512_dm8_nh8_el8_dl1024_df1_fctimeF_ebTrue_dttest_projection_0
+
+results/test_iTransformer_custom_MS_ft15_sl1_ll3_pl512_dm8_nh8_el8_dl1024_df1_fctimeF_ebTrue_dttest_projection_0
+result_long_term_forecast.txt
 # C extensions
 *.so
-
+input/test/data.csv
+input/test/scaler.pkl
+input/train/data.csv
+input/train/scaler.pkl
 */.DS_Store
 
 # Distribution / packaging
@@ -128,4 +135,4 @@ venv.bak/
 dmypy.json
 
 # Pyre type checker
-.pyre/
+.pyre/

LICENSE

@@ -1,6 +1,7 @@
 MIT License
 
 Copyright (c) 2022 THUML @ Tsinghua University
+Copyright (c) 2024 cloner174 @ Hamed Hajipour
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

data_provider/data_factory.py

@@ -16,39 +16,58 @@
 def data_provider(args, flag):
     Data = data_dict[args.data]
     timeenc = 0 if args.embed != 'timeF' else 1
-
-    if flag == 'test':
-        shuffle_flag = False
-        drop_last = True
-        batch_size = 1  # bsz=1 for evaluation
-        freq = args.freq
-    elif flag == 'pred':
+    if flag == 'pred' :
         shuffle_flag = False
         drop_last = False
         batch_size = 1
         freq = args.freq
         Data = Dataset_Pred
+        data_set = Data(
+            root_path=args.pred_root_path,
+            data_path=args.pred_data_path,
+            flag=flag,
+            size=[args.seq_len, args.label_len, args.pred_len],
+            features=args.features,
+            target=args.target,
+            timeenc=timeenc,
+            freq=freq,
+            kind_of_scaler=args.kind_of_scaler if hasattr(args, 'kind_of_scaler') else 'standard',
+            name_of_col_with_date = args.name_of_col_with_date if hasattr(args, 'name_of_col_with_date') else 'date',
+            scale = args.scale if hasattr(args, 'scale') else True,
+            max_use_of_row = args.max_use_of_row if hasattr(args, 'max_use_of_row') else 'No Lim',
+        )
+        print(flag, len(data_set))
     else:
-        shuffle_flag = True
-        drop_last = True
-        batch_size = args.batch_size  # bsz for train and valid
-        freq = args.freq
-
-    data_set = Data(
-        root_path=args.root_path,
-        data_path=args.data_path,
-        flag=flag,
-        size=[args.seq_len, args.label_len, args.pred_len],
-        features=args.features,
-        target=args.target,
-        timeenc=timeenc,
-        freq=freq,
-    )
-    print(flag, len(data_set))
+        if flag == 'test':
+            shuffle_flag = False
+            drop_last = True
+            batch_size = 1  # bsz=1 for evaluation
+            freq = args.freq
+        else:
+            shuffle_flag = True
+            drop_last = True
+            batch_size = args.batch_size  # bsz for train and valid
+            freq = args.freq
+        data_set = Data(
+            root_path=args.root_path,
+            data_path=args.data_path,
+            flag=flag,
+            size=[args.seq_len, args.label_len, args.pred_len],
+            features=args.features,
+            target=args.target,
+            timeenc=timeenc,
+            freq=freq,
+            test_size = args.test_size if hasattr(args, 'test_size') else 0.2,
+            kind_of_scaler= args.kind_of_scaler if hasattr(args, 'kind_of_scaler') else 'standard',
+            name_of_col_with_date = args.name_of_col_with_date if hasattr(args, 'name_of_col_with_date') else 'date',
+            scale = args.scale if hasattr(args, 'scale') else True,
+        )
+        print(flag, len(data_set))
     data_loader = DataLoader(
         data_set,
         batch_size=batch_size,
         shuffle=shuffle_flag,
         num_workers=args.num_workers,
         drop_last=drop_last)
+
     return data_set, data_loader