TIM - Automatic Model Building - Machine Learning
TangentWorks
TIM - Automatic Model Building - Machine Learning
TangentWorks
TIM - Automatic Model Building - Machine Learning
TangentWorks
A predictive model building engine for forecasting and anomaly detection on time series data
TIM, the Tangent Information Modeler, is a predictive model building engine that automates the forecasting and anomaly detection process by analyzing time series data and generating accurate models based on the patterns it detects. It has its own interface, TIM Studio, and is integrated in different platforms.
Highlights
· Fast and accurate forecasting and anomaly detection on time series data
· High quality, explainable models on continuously evolving data in any circumstances
· Easy deployment, an intuitive user interface and platform integration
TIM empowers users to take informed decisions and improve processes, driving business value from predictive analytics. TIM allows users to adapt to new situations by allowing models to be rebuilt or recalibrated continuously. Whereas model recalibration only adjusts the parameters of the model and leaves the structure of the model (features) intact, model rebuilding starts by identifying new features and then builds a completely new model. Through the identification of new features, the process of model rebuilding is made robust to changes in the underlying processes. An important aspect in achieving high quality results, lies in TIM's ability to not only take past values of the target variable as input, but also various additional variables that might relate to the target variable.
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About Tangent Works
Tangent Works delivers forecasting and anomaly detection capabilities for time series data in a fast, accurate and explainable way. This enables users to drive business value from predictive analytics, empowers them to take informed decisions and helps them improve processes.
TIM has already been recognized as a winner in multiple competitions, including GEFCom 2017 and the 2017 ANDRITZ Hackathon.