test1

app.py

@@ -19,28 +19,46 @@ def prepare_data_chronos(data):
     formatted_df = pd.DataFrame({
         'item_id': ['stock'] * len(df),
         'timestamp': pd.to_datetime(df['Date']),
-        'value': df['Close'].astype('float32')  # 使用 'value' 而不是 'target'
+        'value': df['Close'].astype('float32').values.ravel()  # 確保是 1-dimensional
     })
     
-    print("Data types:", formatted_df.dtypes)
-    print("Sample data:", formatted_df.head())
     # 按照時間戳排序
     formatted_df = formatted_df.sort_values('timestamp')
     
     try:
-        # 使用 from_data_frame 方法創建 TimeSeriesDataFrame
-        ts_df = TimeSeriesDataFrame.from_data_frame(
-            formatted_df,
-            id_column='item_id',
-            timestamp_column='timestamp',
-            target_column='value'
-        )
+        # 直接創建 TimeSeriesDataFrame
+        ts_df = TimeSeriesDataFrame(formatted_df)
+        
+        # 打印一些診斷信息
+        print("TimeSeriesDataFrame shape:", ts_df.shape)
+        print("Value column shape:", ts_df['value'].shape)
+        
         return ts_df
     except Exception as e:
         print(f"Error creating TimeSeriesDataFrame: {str(e)}")
+        print("Formatted DataFrame info:")
+        print(formatted_df.info())
         raise
 
 
+# def prepare_data_chronos(data):
+#     # 直接使用收盤價序列
+#     series = pd.Series(
+#         data['Close'].values,
+#         index=data.index,
+#         name='value'
+#     )
+    
+#     # 創建基本的時間序列數據框
+#     df = pd.DataFrame({
+#         'timestamp': series.index,
+#         'value': series.values,
+#         'item_id': ['stock'] * len(series)
+#     })
+    
+#     return TimeSeriesDataFrame(df)
+
+
 # Function to fetch stock indices (you already defined these)
 def get_tw0050_stocks():
     response = requests.get('https://answerbook.david888.com/TW0050')