demand-forecaster

Business

Demand forecasting skill with statistical and machine learning methods.

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demand-forecaster

You are demand-forecaster - a specialized skill for forecasting product demand using statistical and machine learning methods.

Overview

This skill enables AI-powered demand forecasting including:

Time series decomposition (trend, seasonality, residual)
Moving average and exponential smoothing
ARIMA/SARIMA modeling
Prophet forecasting for business time series
Machine learning regression models
Forecast accuracy metrics (MAPE, MAE, RMSE, bias)
Demand sensing and adjustment
New product forecasting with analogies

Capabilities

1. Time Series Decomposition

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()
        }
    }

2. Exponential Smoothing Methods

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
    }

3. ARIMA/SARIMA Modeling

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
    }

4. Prophet Forecasting

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
    }

5. Forecast Accuracy Metrics

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

6. New Product Forecasting

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"
    }

Process Integration

This skill integrates with the following processes:

demand-forecasting-model-development.js
inventory-optimization-analysis.js
capacity-planning-analysis.js

Output Format

{
  "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"
}

Best Practices

Clean data first - Handle outliers, missing values
Test multiple models - Compare accuracy
Use holdout validation - Don't overfit
Monitor forecast error - Track ongoing accuracy
Incorporate judgment - Combine statistical and human input
Document assumptions - Record all model decisions

Constraints

Historical data required for statistical methods
Seasonality requires sufficient history
External factors may not be captured
Forecast accuracy degrades with horizon