fix: correct numpy median/quantile calls in inference pipeline

Fixed critical bug where NumPy arrays were incorrectly called with
.median() and .quantile() as methods instead of np.median() and
np.quantile() functions. This was causing forecasts to return empty
results (only timestamps, no predictions).

Changes:
- Added numpy import
- Changed forecast_numpy.median() to np.median(forecast_numpy, axis=0)
- Changed forecast_numpy.quantile() to np.quantile(forecast_numpy, q, axis=0)

This fix enables the HF Space to generate actual forecast predictions.

src/forecasting/chronos_inference.py

@@ -10,6 +10,7 @@ from typing import List, Dict, Optional
 from datetime import datetime, timedelta
 import polars as pl
 import pandas as pd
+import numpy as np
 import torch
 from datasets import load_dataset
 from chronos import ChronosPipeline
@@ -186,9 +187,9 @@ class ChronosInferencePipeline:
 
                 # Store results
                 results['borders'][border] = {
-                    'median': forecast_numpy.median(axis=0).tolist(),
-                    'q10': forecast_numpy.quantile(0.1, axis=0).tolist(),
-                    'q90': forecast_numpy.quantile(0.9, axis=0).tolist(),
+                    'median': np.median(forecast_numpy, axis=0).tolist(),
+                    'q10': np.quantile(forecast_numpy, 0.1, axis=0).tolist(),
+                    'q90': np.quantile(forecast_numpy, 0.9, axis=0).tolist(),
                     'inference_time_s': time.time() - border_start
                 }