AI Demand Forecasting for Fashion Collections

How We Predict Demand for Fashion Collections?

Short SKU lifecycle (6–12 weeks), heavy dependence on trends and weather, and no historical data for new articles make traditional planning methods ineffective. ARIMA and exponential smoothing yield WAPE >50% on new items — for fashion, that’s a loss. Our attribute-based approach with LightGBM achieves WAPE <30% for new items, and with trend signals, it drops below 25%. That’s 1.7 times more accurate than ARIMA. This reduces overstocks and stockouts by 20–35%. For a network with $10M turnover, savings reach $350,000 per year. Over 5 years, we’ve delivered 30+ projects in retail and e-commerce, from mass-market to premium segments. We guarantee forecast accuracy within ±10% sell-through rate for established SKUs.

Fashion Forecasting Challenges

Cold Start and Short Lifecycle

New collection — no historical sales. Solutions:

• Attribute-based forecasting: predict via characteristics (color, pattern, category, price tier)
• Transfer learning: use a similar article from last season as anchor
• Analogous items: cluster new items with existing SKUs that have history

Classical time series require long history. Instead, we use cross-sectional models at the SKU level. LightGBM on attributes gives WAPE <30% on new items — twice as accurate as ARIMA.

Seasonality and Trends

# Decomposition sales signal # Sales = Seasonal × Category Trend × Fashion Trend × Price Effect × Random # Fashion Trend: external signals (Instagram, Vogue, runway) 

Data Sources

Internal:

• Weekly POS data: sales, returns, discounts
• Inventory data: stock levels, out-of-stock dates
• Product attributes: category, brand, color, material, sizes, price

External Trend Signals:

• Google Trends: search volume dynamics by category
• Instagram/Pinterest: engagement on fashion content (via API or scraping)
• Runway analysis: trend detection from fashion shows (CV on photos from ModaOperandi, Vogue Runway)
• Weather data: temperature directly affects jacket/swimsuit sales

Social Listening:

trend_features = { 'google_trends_category_4w': trends_api_value, 'instagram_hashtag_growth': hashtag_weekly_growth_rate, 'search_volume_brand': keyword_planner_volume, 'temperature_deviation': weather_vs_seasonal_norm, 'competitor_stockout_signal': scraped_inventory_depletion } 

Forecasting Models

Attribute-based LightGBM

For each new item, predict peak week sales and sell-through rate based on attributes + trend features. Trained on historical collections.

Cluster + Analogous Item

from sklearn.cluster import KMeans # Clustering by attribute embedding def find_analogous_items(new_item_features, historical_items, n_clusters=50): kmeans = KMeans(n_clusters=n_clusters) labels = kmeans.fit_predict(historical_items['features']) new_cluster = kmeans.predict([new_item_features])[0] analogs = historical_items[labels == new_cluster] return analogs.sort_values('similarity_score', ascending=False).head(5) 

Life Cycle Curve Clustering

Not all articles are the same. Cluster life cycle curves:

• Type A: fast start → gradual decline (bestseller)
• Type B: slow start → peak at week 4 (niche item)
• Type C: steady sales, basic items

Forecast curve shape → distribute orders over time. Model comparison:

Pre-Season and In-Season Adjustment

Pre-Season Planning (6–9 months before start)

• Initial order based on attribute forecast
• Buy quantities by size grid (size curve model)
• Open-to-buy budget by category

How In-Season Adjustment Improves Forecast?

After the first 2–3 weeks of actual sales, apply Bayesian update to the initial forecast:

def bayesian_forecast_update(prior_forecast, observed_sales, sell_through_weeks): """ Update forecast based on early weeks Sell-through rate in first 2 weeks = strong predictor of final result """ early_st_rate = observed_sales / prior_forecast[:sell_through_weeks].sum() scaling_factor = early_st_rate ** 0.7 # regression to mean return prior_forecast * scaling_factor 

Reorder and Markdown Triggers

• If sell-through > 70% at week 4 → reorder (if production cycle allows)
• If sell-through < 30% at week 6 → start markdowns per markdown calendar

Bayesian update after 2 weeks of sales improves forecast accuracy by 40% — avoiding both shortage and surplus.

Size Distribution

Size Curve Modeling

Historically: XS:S:M:L:XL = 5:20:35:25:15 for a given category. ML adjusts by region, channel, and price tier:

size_curve = lgbm.predict_proba( category=category, price_tier=price_tier, channel=['online', 'store'], region=region ) # → optimal size ratio in order 

The last-size problem: stockout on one size = lost sale. Optimization: small buffer for sizes with lowest availability.