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Model building

TIM strives to follow all the best practices in time series modeling to achieve the best possible accuracy as fast as possible. The architectural design of the model building phase of TIM engine is illustrated in the image below.

architecture

Some of the specifics of this architectural design are discussed in the following subsections. To learn more about how the model building phase fits into the overall TIM (RT)InstantML Forecasting solution read the Overview section.

Expansion

TIM is designed to go through all the predictors a user includes in the dataset and understand which of these predictors are important and which do not contribute to the final results. In the process of expansion, TIM creates many new features from the original predictors to enhance the final model's performance. This is done through a set of common transformations called dictionaries. If some of the features turn out to be useless, TIM can recognise this without compromising on accuracy.

Reduction

After the previously discussed process of expansion, many new features have been created. It is not optimal to retain all of these new features in our final model, as many of them will be highly correlated. Having highly correlated features in a model makes it highly unstable: a small change in a feature could result in a structural change of the whole model, because the model's results are influenced by many similar features (correlated with the one that initially changed). This is not desirable. Therefore, only the most important subset of features will be retained for the final model, eliminating the high correlation among various features.

Increasing model stability by eliminating inner correlation is similar to fighting a phenomenon called overfitting.

Choosing a smaller subset of variables/features - i.e. the process of reduction - is a widely researched topic; algorithms like LASSO, PCA and forward regression are all well known to the public. TIM uses a similar technique that heavily relies on a geometrical perspective and incorporates a tweaked Bayesian Information Criterion.

Multi situational layer and ModelZOO

See Multi-situational layer and ModelZOO section.