Forecasting Demand in High-Volatility Markets
Advanced time-series and transformer-based forecasting for unpredictable commerce ecosystems.
Advanced time-series and transformer-based forecasting for unpredictable commerce ecosystems.
Demand forecasting has always been central to commerce, but the assumptions behind classical forecasting models—smooth trends, stable seasonality, and relatively low volatility—no longer hold for modern global e-commerce. Merchants operating across US, EU, APAC, and LATAM must navigate flash sales, social virality, cross-border logistics constraints, macroeconomic shocks, and increasingly fragmented payment behaviors.
In this paper, we present a high-volatility demand forecasting framework designed for the Tanqory platform. The framework combines advanced time-series models (N-BEATS, DeepAR), transformer-based architectures (Temporal Fusion Transformer, PatchTST, multi-scale transformers), and multi-region calibration layers to forecast demand under extreme uncertainty. Using anonymized multi-region data from merchants on Tanqory, we compare classical baselines (ARIMA, Holt-Winters) with modern deep and transformer-based models across multiple volatility regimes and forecast horizons.
Our experimental results show that transformer-based models reduce forecasting error by 24–39% on average compared to classical baselines, with the largest gains in APAC flash-sale scenarios and LATAM currency-driven volatility. Multi-region calibration further reduces cross-region drift and improves robustness under distribution shifts. We conclude with design recommendations for deploying these models in production, considerations for fairness and privacy, and future research directions towards foundation models for time-series in commerce.
Forecasting demand is a foundational capability for any commerce platform. Decisions that depend on accurate forecasts include:
In traditional retail, demand was often modeled as a relatively smooth combination of trend, seasonality, and residual noise. Today, global e-commerce operates under fundamentally different conditions:
Classical forecasting methods such as ARIMA or Holt-Winters are not designed for such high-volatility, non-stationary, regime-switching environments. This paper aims to:
Traditional approaches include:
These models assume relative stationarity, stable seasonal patterns, and gradual shifts. They struggle with multiple, overlapping sources of volatility and non-linear effects.
Deep models extend forecasting capacity by capturing non-linear relationships and long-range dependencies:
These models improve performance over classical baselines but still face challenges under extreme regime changes and multi-region heterogeneity.
Transformers, originally designed for language, use self-attention to capture long-range dependencies and multi-scale patterns. Variants adapted to time-series include:
Key benefits in commerce forecasting:
Forecasts in commerce must distinguish between organic demand trends and intervention-driven shifts (e.g., promotions, policy changes, logistics constraints). Causal forecasting combines:
This is crucial in high-volatility periods where events are dense and overlapping.
Through internal analysis and review of prior work, we identify several gaps:
Volatility Regime Mismatch
Classical models assume relatively smooth dynamics; they underfit flash sales, viral spikes, and sudden drops, leading to systematic bias in safety-stock planning.
Cross-Region Heterogeneity
Demand drivers differ across US, EU, APAC, and LATAM—e.g., payment reliability in LATAM, flash events in APAC, regulation in EU. Global models without explicit region-aware calibration can misgeneralize and overfit to dominant regions.
Limited Use of Exogenous Signals
Many deployed forecasts ignore critical covariates such as promotions, prices, holidays, supply constraints, or macro indicators, treating all variation as random noise.
Shock and Regime-Change Handling
Conventional models treat large deviations as outliers; in commerce, these are often the most economically important points.
Lack of Interpretability for Operators
Forecasts that cannot explain why demand is expected to rise or fall are hard to trust and act upon at scale.
Latency and Scalability Constraints
High-volume merchants require near real-time updates (e.g., hourly) across thousands of SKUs and multiple regions while keeping computational cost manageable.
We propose a Tanqory-native demand forecasting stack comprising five layers:
We introduce region embeddings that encode:
These embeddings are concatenated with product embeddings and time features (day-of-week, week-of-year, event indicators) to form the model input.
We evaluate three model families:
The final architecture for high-volatility products uses a multi-scale transformer with:
Outputs are post-processed to:
The system supports:
We construct a benchmark using anonymized Tanqory merchant data:
| Region | Merchants | Time Span | Series (SKU×store) | Resolution |\n| --- | --- | --- | --- | --- |\n| US | 110 | 24 months | 85k+ | Daily |\n| EU | 80 | 24 months | 64k+ | Daily |\n| APAC | 160 | 24 months | 120k+ | Daily/Hourly for high-volume SKUs |\n| LATAM | 70 | 24 months | 52k+ | Daily |
We segment items into low, medium, and high-volatility regimes based on rolling variance and a volatility index that incorporates promotion density and stock-out frequency.
Across all regions and volatility regimes, we observe:
APAC:
LATAM:
EU:
US:
By comparing forecast residuals during known shock periods, we find that:
Practical benefits observed in offline simulations and pilot deployments include:
Case 1 – APAC 11.11 Event
A consumer electronics merchant experienced a 10–15× increase in orders during 11.11. Classical models substantially under-forecast peak demand. The multi-scale transformer, trained with region embeddings and explicit event flags, predicted peak demand within ~12% error, while ARIMA under-forecasted by more than 50%.
Case 2 – LATAM Currency Volatility
For a fashion merchant priced in local currency with USD-indexed costs, demand sensitivity to exchange-rate movements was high. DeepAR with price and FX covariates captured asymmetric responses (demand dropped steeply beyond certain price points). Transformers benefited from the same covariates and produced narrower but well-calibrated prediction intervals, leading to more confident restocking decisions.
Case 3 – EU Regulatory Transition
A new cross-border VAT rule caused a drop in orders from certain destinations. N-BEATS and ARIMA initially treated this as noise. A transformer model with policy-change indicators adjusted the forecast trend more quickly, leading to earlier repricing and shipping policy adjustments.
Case 4 – US Social Virality
A niche product gained sudden popularity through short-form video. Historical patterns were not predictive. Attention layers in transformers highlighted the recent surge and allocated more weight to the latest sequence, leading to faster upward forecast revision than classical smoothing methods.
This helps merchants understand that forecasts are scenario-dependent, not deterministic guarantees.
High-volatility markets are now the norm rather than the exception in global e-commerce. Static and purely statistical approaches are no longer sufficient for demand forecasting that must support real-time, multi-region, multi-vertical operations.
By combining transformer-based forecasting, probabilistic time-series models, and multi-region calibration, Tanqory can provide merchants with more accurate, robust, and interpretable demand forecasts. This, in turn, enables better inventory allocation, reduced operational risk, and more effective marketing and pricing strategies across US, EU, APAC, and LATAM.
Forecasting becomes not just a back-office statistical task, but a strategic AI capability at the core of modern commerce infrastructure.
(Representative, non-exhaustive references in forecasting and time-series ML)
