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@@ -63,7 +63,7 @@ def forecast_data_distribution(pre_data,args):
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def model_prediction(df,args):
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if 'is_limit' in df.columns:
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df = df[df['is_limit'] == False]
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- features, time_steps, col_time, model_name,col_reserve,howlongago = str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name'],str_to_list(args['col_reserve'])
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+ features, time_steps, col_time, model_name,col_reserve,howlongago = str_to_list(args['features']), int(args['time_steps']),args['col_time'],args['model_name'],str_to_list(args['col_reserve']),int(args['howlongago'])
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feature_scaler,target_scaler = get_scaler_model_from_mongo(args)
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df = df.sort_values(by=col_time).fillna(method='ffill').fillna(method='bfill')
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scaled_features = feature_scaler.transform(df[features])
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