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Demand forecasting skill with statistical and machine learning methods.
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SOC 직업 분류 기준
| name | demand-forecaster |
| description | Demand forecasting skill with statistical and machine learning methods. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"supply-chain","backlog-id":"SK-IE-024"} |
| graph | {"domains":["domain:industrial-engineering"],"skillAreas":["skill-area:statistical-analysis","skill-area:organizational-design","skill-area:data-analysis"],"roles":["role:operations-analyst","role:research-engineer"]} |
You are demand-forecaster - a specialized skill for forecasting product demand using statistical and machine learning methods.
This skill enables AI-powered demand forecasting including:
import numpy as np
import pandas as pd
from statsmodels.tsa.seasonal import seasonal_decompose
def decompose_demand(data: pd.Series, period: int = 12, model: str = 'additive'):
"""
Decompose time series into trend, seasonal, and residual components
model: 'additive' or 'multiplicative'
"""
decomposition = seasonal_decompose(data, model=model, period=period)
return {
"trend": decomposition.trend,
"seasonal": decomposition.seasonal,
"residual": decomposition.resid,
"model": model,
"period": period,
"summary": {
"trend_range": (decomposition.trend.min(), decomposition.trend.max()),
"seasonal_amplitude": decomposition.seasonal.max() - decomposition.seasonal.min(),
"residual_std": decomposition.resid.std()
}
}
from statsmodels.tsa.holtwinters import ExponentialSmoothing, SimpleExpSmoothing
def simple_exponential_smoothing(data: pd.Series, alpha: float = None):
"""
Simple Exponential Smoothing (SES) for level-only data
"""
model = SimpleExpSmoothing(data)
if alpha:
fit = model.fit(smoothing_level=alpha, optimized=False)
else:
fit = model.fit(optimized=True)
return {
"model": "SES",
"alpha": fit.params['smoothing_level'],
"fitted_values": fit.fittedvalues,
"forecast_method": fit
}
def holt_winters(data: pd.Series, seasonal_periods: int = 12,
trend: str = 'add', seasonal: str = 'add'):
"""
Holt-Winters Exponential Smoothing with trend and seasonality
"""
model = ExponentialSmoothing(
data,
trend=trend,
seasonal=seasonal,
seasonal_periods=seasonal_periods
)
fit = model.fit(optimized=True)
return {
"model": "Holt-Winters",
"parameters": {
"alpha": fit.params.get('smoothing_level'),
"beta": fit.params.get('smoothing_trend'),
"gamma": fit.params.get('smoothing_seasonal')
},
"fitted_values": fit.fittedvalues,
"forecast_method": fit
}
def forecast_exponential_smoothing(fit, periods: int):
"""Generate forecast from fitted model"""
forecast = fit.forecast(periods)
return {
"forecast": forecast,
"periods": periods
}
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
import pmdarima as pm
def auto_arima(data: pd.Series, seasonal: bool = True, m: int = 12):
"""
Automatic ARIMA model selection
"""
model = pm.auto_arima(
data,
seasonal=seasonal,
m=m,
stepwise=True,
suppress_warnings=True,
error_action='ignore',
trace=False
)
return {
"order": model.order,
"seasonal_order": model.seasonal_order if seasonal else None,
"aic": model.aic(),
"bic": model.bic(),
"model": model
}
def fit_sarima(data: pd.Series, order: tuple, seasonal_order: tuple):
"""
Fit SARIMA model with specified orders
order: (p, d, q)
seasonal_order: (P, D, Q, s)
"""
model = SARIMAX(data, order=order, seasonal_order=seasonal_order)
fit = model.fit(disp=False)
return {
"model": "SARIMA",
"order": order,
"seasonal_order": seasonal_order,
"aic": fit.aic,
"bic": fit.bic,
"fitted_values": fit.fittedvalues,
"residuals": fit.resid,
"forecast_method": fit
}
def forecast_arima(fit, periods: int, conf_level: float = 0.95):
"""Generate ARIMA forecast with confidence intervals"""
forecast = fit.get_forecast(periods)
ci = forecast.conf_int(alpha=1-conf_level)
return {
"forecast": forecast.predicted_mean,
"lower_ci": ci.iloc[:, 0],
"upper_ci": ci.iloc[:, 1],
"confidence_level": conf_level
}
from prophet import Prophet
def prophet_forecast(data: pd.DataFrame, periods: int,
yearly_seasonality: bool = True,
weekly_seasonality: bool = False,
holidays: pd.DataFrame = None):
"""
Facebook Prophet forecasting
data: DataFrame with columns 'ds' (date) and 'y' (value)
"""
model = Prophet(
yearly_seasonality=yearly_seasonality,
weekly_seasonality=weekly_seasonality,
daily_seasonality=False
)
if holidays is not None:
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=periods, freq='M')
forecast = model.predict(future)
return {
"model": "Prophet",
"forecast": forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']],
"components": {
"trend": forecast['trend'],
"yearly": forecast.get('yearly', None),
"weekly": forecast.get('weekly', None)
},
"prophet_model": model
}
def calculate_forecast_accuracy(actual: np.array, forecast: np.array):
"""
Calculate comprehensive forecast accuracy metrics
"""
actual = np.array(actual)
forecast = np.array(forecast)
errors = actual - forecast
n = len(actual)
# Mean Absolute Error
mae = np.mean(np.abs(errors))
# Root Mean Square Error
rmse = np.sqrt(np.mean(errors**2))
# Mean Absolute Percentage Error (handle zeros)
with np.errstate(divide='ignore', invalid='ignore'):
ape = np.abs(errors / actual) * 100
ape = np.where(np.isfinite(ape), ape, 0)
mape = np.mean(ape)
# Weighted MAPE (weighted by actual values)
wmape = np.sum(np.abs(errors)) / np.sum(actual) * 100
# Bias (Mean Error)
bias = np.mean(errors)
bias_percent = (bias / np.mean(actual)) * 100
# Tracking Signal
cumulative_error = np.sum(errors)
mad = np.mean(np.abs(errors))
tracking_signal = cumulative_error / mad if mad > 0 else 0
return {
"MAE": round(mae, 2),
"RMSE": round(rmse, 2),
"MAPE": round(mape, 2),
"WMAPE": round(wmape, 2),
"Bias": round(bias, 2),
"Bias_Percent": round(bias_percent, 2),
"Tracking_Signal": round(tracking_signal, 2),
"interpretation": interpret_accuracy(mape, bias_percent, tracking_signal)
}
def interpret_accuracy(mape, bias_pct, tracking_signal):
interpretations = []
if mape < 10:
interpretations.append("Excellent accuracy (MAPE < 10%)")
elif mape < 20:
interpretations.append("Good accuracy (MAPE 10-20%)")
elif mape < 30:
interpretations.append("Fair accuracy (MAPE 20-30%)")
else:
interpretations.append("Poor accuracy (MAPE > 30%)")
if abs(bias_pct) > 5:
direction = "over" if bias_pct < 0 else "under"
interpretations.append(f"Systematic {direction}-forecasting (Bias {bias_pct:.1f}%)")
if abs(tracking_signal) > 4:
interpretations.append("Tracking signal out of control - model review needed")
return interpretations
def analogy_forecast(analogous_product_history: pd.Series,
new_product_attributes: dict,
analog_attributes: dict):
"""
Forecast new product demand using analogous product history
"""
# Calculate scaling factors based on attribute differences
scaling_factors = {}
# Price adjustment
if 'price' in new_product_attributes and 'price' in analog_attributes:
price_ratio = analog_attributes['price'] / new_product_attributes['price']
# Price elasticity approximation
scaling_factors['price'] = price_ratio ** 1.5 # Assuming elasticity of -1.5
# Market size adjustment
if 'market_size' in new_product_attributes:
scaling_factors['market'] = (new_product_attributes['market_size'] /
analog_attributes.get('market_size', 1))
# Calculate combined scaling factor
combined_factor = np.prod(list(scaling_factors.values())) if scaling_factors else 1.0
# Generate forecast
forecast = analogous_product_history * combined_factor
return {
"method": "Analogy",
"analogous_product": analog_attributes.get('name', 'Unknown'),
"scaling_factors": scaling_factors,
"combined_factor": combined_factor,
"forecast": forecast,
"confidence": "Low - based on single analogy",
"recommendation": "Collect actual data as soon as possible to refine"
}
This skill integrates with the following processes:
demand-forecasting-model-development.jsinventory-optimization-analysis.jscapacity-planning-analysis.js{
"forecast_model": "SARIMA(1,1,1)(1,1,1,12)",
"forecast_periods": 12,
"forecast": [120, 135, 142, ...],
"confidence_intervals": {
"lower": [110, 125, 130, ...],
"upper": [130, 145, 154, ...]
},
"accuracy_metrics": {
"MAPE": 8.5,
"RMSE": 15.2,
"Bias": -2.1
},
"interpretation": "Excellent accuracy with slight under-forecasting tendency"
}