| import numpy as np
|
| import pandas as pd
|
|
|
|
|
| def generate_synthetic_sku(
|
| n_periods: int = 52,
|
| base_price: float = 10.0,
|
| base_demand: float = 120.0,
|
| elasticity: float = -1.5,
|
| noise_std: float = 0.1,
|
| seed: int = 42,
|
| ) -> pd.DataFrame:
|
| """
|
| Generate a single-SKU synthetic price–demand time series.
|
| Elasticity is constant and known (ground truth).
|
| """
|
| rng = np.random.default_rng(seed)
|
|
|
|
|
| prices = base_price * (1 + rng.normal(0, 0.15, size=n_periods))
|
| prices = np.clip(prices, base_price * 0.6, base_price * 1.4)
|
|
|
|
|
| noise = np.exp(rng.normal(0, noise_std, size=n_periods))
|
| demand = base_demand * (prices ** elasticity) * noise
|
|
|
| df = pd.DataFrame(
|
| {
|
| "t": np.arange(n_periods),
|
| "price": prices,
|
| "qty": demand,
|
| }
|
| )
|
|
|
| return df
|
|
|
