Acclaimed Labs
Proprietary Forecasting Engine + AI Calibration
Acclaimed Labs has built what few others have: a proven, proprietary forecasting engine that consistently generates predictive alpha on its own. Unlike generic AI-first models, our system was designed from the ground up in R and Python, automatically testing, pruning, and stacking thousands of statistical models until the strongest ensemble emerges. This engine has already outperformed many AI-only approaches in accuracy and stability.
On top of this foundation, we apply a transparent AI overlay. Rather than replacing the forecast, the AI layer functions as a calibration and health-check system: learning from residuals, flagging bias, and documenting results. This dual-layer design means clients receive forecasts that are not only more accurate, but also auditable, explainable, and defensible.
Statistical Modeling at Scale
Our baseline engine is built on rigorous statistical methods: ARIMA, ETS, state-space, and other time-series families. Thousands of candidate models are automatically generated, tested, pruned, and stacked to produce a powerful ensemble. This process ensures that no single model dominates — the result is stability, accuracy, and robustness even in volatile environments.
Unlike most competitors, this system already demonstrated alpha without AI. It is the bedrock of our solution: a rare independent forecasting engine that stands on its own merits.
What the AI Overlay Adds
- Analyzes forecast vs. actual residuals to spot systematic bias
- Leverages explanatory variables (factors, sensors, EEG, telemetry)
- Improves forecast stability across regimes and domains
- Documents the forecasting process with logs and model cards
This makes the combined system stronger than either piece alone: alpha-proven forecasts + AI calibration = accuracy and auditability.
End-to-End Pipeline
Clients bring their data — whether it’s finance, ISR, health, or robotics — and we stand up a reproducible forecasting pipeline:
- Ingest: Raw CSVs or API feeds, schema validation, outlier handling
- Baseline: Automated univariate modeling, pruning, and ensemble creation
- Overlay: Multivariate AI calibration with aligned variables
- Outputs: Forecast files, sealed logs, optional strategy notes
This process is fully automated and can be deployed in the cloud for ongoing forecasts. Setup is measured in weeks, not years.
Why Better Forecasting Matters
Accurate prediction is universally valuable. With foresight, clients can:
- Save money by anticipating demand and allocating resources more efficiently
- Improve client service through advance planning and readiness
- Reduce waste by avoiding overcommitment or underutilization
- Make faster, data-driven decisions across domains
Whether forecasting prices, mission risks, patient episodes, or machine downtime, the principle is the same: better forecasts create real-world advantage.
Quant Finance
Weekly S&P 500 forecasts, Top-10/Bottom-10 strategies, factor timing, turnover-aware pipelines. Inputs include price history, volume, volatility, and sector indices. Outputs: reproducible forecasts, ranks, and logs suitable for Tier-1 quant review.
ISR & Mission Planning
Fusion of multi-sensor data (radar, sat, comms, terrain, weather) into activity windows, anomaly alerts, and logistics schedules. Outputs are reproducible, red-teamable, and help operators anticipate shifts before they occur.
Health & BCI
Forecasting EEG stability, biometric signals, and episode likelihoods. Enables clinicians to detect decoder drift, plan recalibration windows, and run IRB-compliant trials with logged forecasts and sealed attestations.
Robotics
Telemetry-based forecasting of torque, latency, battery, and trajectories. Allows operators to predict maintenance windows, avoid downtime, and plan missions with higher confidence.
Case Study — Finance
A Tier-1 quant fund needed forecasts for all S&P 500 names, reproducible and rank-stable. The Acclaimed Labs engine produced alpha out of the box. Adding AI calibration reduced error further while preserving rank order.
| Model | MAE | Hit Rate | IC |
|---|---|---|---|
| Baseline | 1.12 | 53% | 0.12 |
| With Overlay | 0.84 | 58% | 0.19 |
Case Study — ISR
An ISR team needed early warnings for operational windows. Our pipeline fused multiple feeds, and the AI overlay extended early-alert lead time by 12 hours while cutting false positives by nearly 30%.
| Metric | Baseline | Overlay |
|---|---|---|
| Early-alert lead time | — | +12h |
| False positives | 1.0× | 0.72× |
| Window accuracy | 74% | 81% |
Case Study — Health & BCI
In a clinical trial setting, drift in EEG decoding threatened data integrity. Our system forecast stability windows and flagged recalibration needs 24h earlier than existing methods, while reducing false alarms.
| Metric | Baseline | Overlay |
|---|---|---|
| Stability detection | 78% | 86% |
| False alarms | 1.0× | 0.68× |
| Recalibration lag | — | -24h |
Cloud Deployment & Customization
The Acclaimed Labs forecasting system can be deployed as a dedicated cloud pipeline. Clients can connect their own data streams securely and receive rolling forecasts at their chosen cadence. Each client pipeline is customizable: dedicated variables, custom schema, and private logs. Delivery is via CSV/Excel, with optional API feeds for direct system integration.
This means a client can stand up their own forecasting environment — automated, scalable, and exclusive — in weeks rather than years.
Built in Maine. Built for Operators.
Headquartered in Maine, Acclaimed Labs reflects resilience and clarity. Our rugged environment informs an operator-first ethos: forecasts that are readable, reproducible, and calm under pressure. From hedge funds to ISR commands, clinical labs to robotics fleets, our mission is the same: deliver foresight clients can act on.