Forecasted time series every day, powering 100,000+ forecasts per month.
TimeGPT surpassed legacy xgboost models in both accuracy and speed with minimal tuning.
Cut the number of required exogenous variables and streamlined integration of historic and future features.
TradeSmith is a financial technology company delivering investment research, portfolio analytics, and predictive tools to over 60,000 active investors.
The platform uses Nixtla’s TimeGPT to forecast more than 100,000 forecasts each month.
By replacing tree-based models in production, TradeSmith achieved higher accuracy, faster predictions, and greater scalability across their analytics suite.
With support from Nixtla’s scientific team, they tailored TimeGPT through professional services to meet their unique investment forecasting needs.
TradeSmith needed to scale forecasting to tens of thousands of time series daily, improve accuracy over legacy models, and simplify the integration of exogenous features.
Nixtla’s TimeGPT was integrated into TradeSmith’s production pipeline, replacing tree-based models and leveraging zero-shot capabilities to deliver reliable forecasts at scale.
TimeGPT outperformed our legacy xgboost models, delivering more reliable forecasts with minimal tuning and scaling effortlessly to half-a-million forecasts per month.