Methodology Behind Our Products
Enterprise cost (spend) and revenue forecasting present unique challenges that extend beyond traditional regression analysis. Complete Intelligence incorporates our extensive global economic and markets database with machine learning algorithms for progressively intelligent results and more confident planning decisions.
We have designed our integrated global model to ensure that actions in one market, country or sector of the economy are reflected elsewhere in markets, industries and the global economy. These functions include, but are not limited to economic indicators, international trade, currencies, commodity prices, equity indices, spot markets, futures markets and more.
Data are sourced from national statistical agencies, multilateral banks, multilateral government bodies, and other publicly available sources. Our data frame includes historical data going back to 2010, covering nearly 1,500 industry sectors along with global economic indicators and hundreds of currencies, commodities and equity indices. These span more than 100 countries for comprehensive global reach and market coverage.
We begin our process with the screening, repair and validation of data with multi-layered, multi-dimensional analyses to understand how data should act to create predictive intelligence scenarios for prices, costs, budgets and revenues. We test economic, industry, market and endogenous factors to compare behaviors of historical data sets within our proprietary machine learning model.
We then employ our ensemble methodology, utilizing ~15 billion data items and testing more than 1 million functions against the historical data series in each forecasted line item. Our platform generates an environment that is a proxy for the global economy, industries, and market events (currency movements, commodity prices, etc.).
The ensemble methodology utilizes many different approaches to account for both fundamental and technical movement of an asset/element. Thousands of scenarios are run to identify optimal performance over multiple historical windows. Accuracy is accounted for in the process with each successive iteration, resulting in robust forecasts for each individual line item, responding to market context and improving performance over time.