Most forecasts fall short because they treat drivers in isolation. Multivariate Time Series in Qlik Predict™ models interconnected factors like promotions, weather, and costs, so forecasts reflect real-world complexity. Analysts can build no-code models in minutes, explain results with SHAP transparency, and trigger proactive actions through Qlik automations. With governance and MLOps built in, models stay trusted and enterprise-ready. Fully embedded in Qlik Cloud, Multivariate Time Series turns analysis into action faster than standalone AutoML tools that stop at raw predictions.
Key Benefits
Plan with Confidence Multivariate forecasting captures the effect of multiple drivers, so decisions reflect the complexity of real markets and operations.
Build Trust in Predictions Transparent explanations of drivers make it easy to communicate insights and secure stakeholder buy-in.
Act Faster Real-time alerts and automations help teams adjust pricing, inventory, staffing, or strategy before issues escalate.
Empower Every Analyst A no-code interface gives business users the ability to build, test, and share forecasts without waiting on scarce data science resources.
Stay Governed and Ready Built-in MLOps ensures models are monitored, approved, and version-controlled for enterprise reliability.
Features
High-Performance Forecasting GPU-accelerated multivariate models with automated tuning deliver fast, scalable results across large datasets.
Explainable AI SHAP-based insights reveal the drivers of each forecast at both record and aggregate levels, making predictions transparent and actionable.
Interactive Scenario Testing Live “what-if” sliders in Qlik Cloud let analysts explore potential outcomes and stress-test decisions before acting.
Built-In Governance Enterprise MLOps includes drift monitoring, version control, and approval workflows to ensure reliable, compliant forecasting at scale.
Seamless Qlik Integration Predictive insights embed directly into Qlik Cloud dashboards and automations, turning analysis into immediate action.
The dataset must respond to some criteria in order to be recognized as usable for Time Series analysis. If not, the options for that kind of analysis won't appear.
