Update stock_analysis.py

stock_analysis.py

@@ -20,14 +20,12 @@ def forecast_series(series, model="ARIMA", forecast_period=FORECAST_PERIOD):
         
         # Get confidence intervals
         conf_int = model_fit.get_forecast(steps=forecast_period).conf_int()
-        lower_ci = conf_int.iloc[:, 0] if isinstance(conf_int, pd.DataFrame) else conf_int[:, 0]
-        upper_ci = conf_int.iloc[:, 1] if isinstance(conf_int, pd.DataFrame) else conf_int[:, 1]
         
         # Ensure all arrays have the same length
-        min_length = min(len(forecast), len(lower_ci), len(upper_ci))
+        min_length = min(len(forecast), conf_int.shape[0])
         predictions = forecast[:min_length]
-        lower_ci = lower_ci[:min_length]
-        upper_ci = upper_ci[:min_length]
+        lower_ci = conf_int.iloc[:min_length, 0] if isinstance(conf_int, pd.DataFrame) else conf_int[:min_length, 0]
+        upper_ci = conf_int.iloc[:min_length, 1] if isinstance(conf_int, pd.DataFrame) else conf_int[:min_length, 1]
         
     elif model == "Prophet":
         # Implement Prophet forecasting method
@@ -38,6 +36,12 @@ def forecast_series(series, model="ARIMA", forecast_period=FORECAST_PERIOD):
     else:
         raise ValueError(f"Unsupported model: {model}")
 
+    # Ensure all arrays are of the same length
+    min_length = min(len(predictions), len(lower_ci), len(upper_ci))
+    predictions = predictions[:min_length]
+    lower_ci = lower_ci[:min_length]
+    upper_ci = upper_ci[:min_length]
+
     return predictions, pd.DataFrame({'Lower_CI': lower_ci, 'Upper_CI': upper_ci})
 
 def get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method, start_date, end_date):
@@ -53,7 +57,13 @@ def get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method,
 
     last_date = pd.to_datetime(series['Date'].values[-1])
     forecast_dates = pd.date_range(start=last_date + timedelta(days=1), periods=len(predictions))
-    forecast_dates = [date for date in forecast_dates if is_business_day(date)][:len(predictions)]
+    forecast_dates = [date for date in forecast_dates if is_business_day(date)]
+
+    # Ensure all data has the same length
+    min_length = min(len(predictions), len(forecast_dates), len(confidence_intervals))
+    predictions = predictions[:min_length]
+    forecast_dates = forecast_dates[:min_length]
+    confidence_intervals = confidence_intervals.iloc[:min_length]
 
     forecast = pd.DataFrame({
         "Date": forecast_dates,