From b929e64b222cd57519513043b47b91ec01ca3a38 Mon Sep 17 00:00:00 2001
From: fhm <rynare.of.legend@gmail.com>
Date: Wed, 9 Jul 2025 05:11:19 +0700
Subject: [PATCH] fix

---
 routes/protected_prediction.py | 6 +++---
 schema/prediction.py           | 1 +
 utils/statistic/auto_arima.py  | 2 +-
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/routes/protected_prediction.py b/routes/protected_prediction.py
index 179d16d..43e5552 100644
--- a/routes/protected_prediction.py
+++ b/routes/protected_prediction.py
@@ -18,7 +18,7 @@ def predict_auto(request: AutoPredictionRequest):
 
         series = df['amount']
 
-        result = auto_arima_forecast(series, forecast_periods=1)
+        result = auto_arima_forecast(series, forecast_periods=request.future_steps)
 
         return AutoPredictionResponse(
             rmse=result["rmse"],
@@ -47,14 +47,14 @@ def predict_manual(request: ManualPredictionRequest):
 
         p, d, q = request.arima_model
 
-        result = manual_arima_forecast(series, p=p, d=d, q=q, forecast_periods=1)
+        result = manual_arima_forecast(series, p=p, d=d, q=q, forecast_periods=request.future_steps)
 
         return ManualPredictionResponse(
             arima_order=tuple(result["arima_order"]),
             prediction=result["prediction"],
             lower=result["lower"],
             upper=result["upper"],
-            success=True
+            success=True,            
         )
 
     except ValueError as ve:
diff --git a/schema/prediction.py b/schema/prediction.py
index 496c917..7ecccd9 100644
--- a/schema/prediction.py
+++ b/schema/prediction.py
@@ -8,6 +8,7 @@ class BasePredictionRequest(BaseModel):
     value_column: str='sold_qty'
     date_column: str='date'
     date_regroup: bool=False
+    future_steps: int=1
 
 
 class AutoPredictionRequest(BasePredictionRequest):
diff --git a/utils/statistic/auto_arima.py b/utils/statistic/auto_arima.py
index 94efb93..a06a44b 100644
--- a/utils/statistic/auto_arima.py
+++ b/utils/statistic/auto_arima.py
@@ -6,7 +6,7 @@ import pandas as pd
 import numpy as np
 
 warnings.filterwarnings("ignore", category=FutureWarning)
-def auto_arima_forecast(series: pd.Series, train_ratio=0.8, forecast_periods: int = 1) -> dict:
+def auto_arima_forecast(series: pd.Series, train_ratio=0.7, forecast_periods: int = 1) -> dict:
     if series is None or series.empty:
         raise ValueError("Data tidak valid atau kosong.")