At Indicio, we’ve built a robust platform designed to revolutionize the way you forecast, analyze, and make data-driven decisions. Our advanced suite of features empowers organizations to go beyond traditional forecasting methods, unlocking deeper insights and providing more accurate predictions for the future.
Explore our core features below and see how they can transform your business:
In the dynamic world of forecasting, choosing the right variables, also called leading indicators or features, can make all the difference between accurate and unreliable forecasts. Our advanced variable selection tool empowers you to identify the most relevant variables, optimizing your forecasting models for accuracy, efficiency, and reliability.
Many organizations rely on correlation to identify leading indicators, but this approach often falls short in producing accurate forecasts.
Correlation only measures the linear relationship between two variables, whereas advanced methods can assess interactions among multiple variables, quantify the contribution of each, and account for group effects. This leads to significantly improved forecast accuracy.
For more insights, check out our interview with Professor Sune Karlsson, a key contributor to research on Bayesian Variable Selection.
Forecasting doesn’t have to be a complex, time-consuming process. With our cutting-edge platform, decision-makers can build powerful forecasting models in a fraction of the time. Our extensive library of models, powered by the latest advancements in forecast research, automates the process—making it accessible to everyone, from data scientists to business leaders.
Building a forecasting model from scratch can take months of coding, only to discover that the model may not provide the highest accuracy. Since you can't predict which model will perform best without applying and benchmarking multiple models, you risk investing significant time on something that might not deliver results.
With our automated model building, you can bypass this uncertainty—quickly generating and testing a variety of advanced models to ensure optimal forecasting accuracy, all in a fraction of the time.
.gif)
In today's rapidly changing business environment, planning for the future is more complex than ever. To navigate uncertainty and make informed decisions, organizations are increasingly turning to Scenario Analysis. This powerful tool enables businesses to explore multiple potential futures, preparing them for a range of possible outcomes.
Many organizations today rely on human judgment or manual manipulation of external variables to create scenarios. While these methods are common, they are often limited by bias and oversimplification, failing to account for complex interdependencies.
Conditional forecasting, as proposed by Waggoner and Zha, offers a more sophisticated alternative using a Bayesian framework. By accounting for parameter uncertainty and providing probability distributions, this approach delivers more accurate, reliable forecasts, enabling organizations to make informed, data-driven decisions in uncertain environments.
Forecasting accuracy is essential, but understanding a forecast behaves a certain way is just as important for building alignment and trust within an organization. With Explainable Forecasting, powered by SHapley Additive exPlanations, you gain clear, actionable insights into the factors affecting your forecasts. This level of transparency not only enhances trust in the forecasts but also empowers decision-makers to take informed, targeted actions based on those insights.
With Explainable Forecasting, you no longer have to choose between complexity and clarity. Our platform provides the transparency you need to explain exactly how and why advanced models make predictions. This means you can leverage cutting-edge forecasting techniques without sacrificing the ability to communicate key drivers to your stakeholders—resulting in both improved accuracy and greater trust.
Aligning forecasts across various levels and functions within your organization can be both complex and time-consuming. Hierarchical forecasting streamlines this process by enabling you to create accurate forecasts at every level—whether it's weekly operational forecasts by product or long-term strategic forecasts of market trends. Once the forecasts are generated, hierarchical forecasting ensures alignment by assigning greater weight to higher-performing forecasts, resulting in cohesive forecasts optimized for accuracy
Hierarchical Forecasting breaks down the organizational silos and unifies the organization towards one source of truth, while improving the forecasting accuracy.
This interview with one of the contributors to the Hierarchical Forecasting research, Nikos Kourentzes, Professor at Skövde University discusses the concept and value of Hierarchical Forecasting.
Experience the ease and accuracy of Indicio’s automated forecasting platform firsthand. Click to start a virtual demo today and discover how our cutting-edge tools can streamline your decision-making process.
We are an automated forecasting software that bridges the latest statistical models from academia with the specific leading indicators in an organization's market environment. Helping your organization achieve the highest forecast accuracy.
© 2025 Indicio Technologies All Rights Reserved