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Open Forecast Engine (OFE) is a tool to perform forecasts for time series, by using Machine Learning and Deep Learning algorithms.

Machine Learning and Deep Learning algorithms.

Official profile on AWS Marketplace website

About the Forecast Engine

The Forecast Engine developed by Morris & Opazo is a generic forecasting tool, based on time series, making predictions on the variables, and delivering behavioral information about the uncertainty of the forecasted time series, expressed in the delivery of prediction intervals.

Challenge and overall solution concept

The forecast of time series has a wide range of applications in the industry, governed by a series of factors depending on the context. For example, in retail there are elements such as weather stations, mass sales and discounts of multiple products, holiday periods according to each country (to name a few), in which classic models fail to explain precisely due to the non-linear nature of these events.

Another factor to consider is the daily collection of large volumes of information due to the growth and technological revolution, transforming each data into a potential asset for the company.

What can you do with this solution?

Our forecasting engine provides an approach to the world of Artificial Intelligence in a simple and concrete way. OFE allows forecasts for one or multiple time series, delivering prediction intervals with 80% probability, and the performance information of the model used, as a descriptive analysis of the series.

How can it help you?

Open Forecasting Engine helps you to make forecasts easily, where the only user interaction is at the stage of entering data and characteristics of the desired forecast. The algorithms on which our product is based use advanced mathematical concepts, which translate into more accurate forecasts capturing complex relationships in the dynamics of time series, helping in the planning, inventory, investment, depending on the application and area studied.


Read the step-by-step instructions in here 


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