Explain a forecast with SHAP

import numpy as np
import pandas as pd

from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    # Defaults to os.environ["NIXTLA_API_KEY"]
    api_key="my_api_key_provided_by_nixtla"
)

rng = np.random.default_rng(7)
n = 365
h = 14
t = np.arange(n + h)

price = 20 + 1.8 * np.sin(2 * np.pi * t / 90) + rng.normal(0, 0.35, n + h)
promotion = ((t % 42) >= 35).astype(int)
temperature = (
    18
    + 9 * np.sin(2 * np.pi * (t - 30) / 365)
    + rng.normal(0, 0.8, n + h)
)
weekly = 6 * np.sin(2 * np.pi * t / 7)
demand = (
    118
    - 2.4 * price[:n]
    + 16 * promotion[:n]
    + 0.55 * temperature[:n]
    + weekly[:n]
    + rng.normal(0, 2.0, n)
)

dates = pd.date_range("2024-01-01", periods=n + h, freq="D")
df = pd.DataFrame(
    {
        "unique_id": "store-a",
        "ds": dates[:n],
        "y": demand,
        "price": price[:n],
        "promotion": promotion[:n],
        "temperature": temperature[:n],
    }
)
X_df = pd.DataFrame(
    {
        "unique_id": "store-a",
        "ds": dates[n:],
        "price": price[n:],
        "promotion": promotion[n:],
        "temperature": temperature[n:],
    }
)