Compare Prescient AI vs. Timestral

Identify underperforming campaigns to swiftly reallocate your advertising budget according to your simulation outcomes while staying within your total budget. Our approach employs a windowless measurement method rooted in probabilistic analysis, utilizing time-series data from both your ecommerce platform and various marketing channels to pinpoint the factors that truly influence performance. By analyzing your brand's historical metrics, you can predict the revenue and return on ad spend (ROAS) for your campaigns at any adjusted budget level before making any changes. Integrate all of your marketing channels in a mere 10 minutes with our straightforward, one-time setup process. Within 48 hours, you'll receive valuable insights that can inform your strategy. Additionally, marketers can assess and compare the effects of cross-channel awareness on organic searches, direct traffic, and other campaigns, enabling a comprehensive understanding of their marketing ecosystem. This approach empowers brands to make data-driven decisions that enhance overall effectiveness.

Timestral represents a suite of time-series foundation models along with a Python inference toolkit designed to create multivariate probabilistic forecasts based on historical data sequences. It comes in four different model sizes—5M, 28M, 123M, and 209M parameters—that are capable of handling past-only, known-future, and categorical covariates, providing quantile forecasts while accommodating context windows as extensive as 32,768 time steps. Users have the flexibility to load pretrained weights either from the Hugging Face Hub or directly from local files, enabling them to perform inference on both CPU and GPU. Such forecasts can be seamlessly integrated into various applications, including monitoring systems, capacity planning, energy management, supply chain logistics, financial risk assessment, or maintenance operations. Furthermore, Timestral offers zero-shot forecasting capabilities and can be tailored to specific domain data through the Mistral Forge. This package is primarily aimed at data scientists, machine learning engineers, and collaborative teams focused on developing robust forecasting solutions; the repository code is licensed under Apache 2.0, while the model weights are governed by a separate Modified MIT license. With its comprehensive features, Timestral is positioned to significantly enhance the accuracy and efficiency of predictive analytics in numerous industries.