Prevendo eventos extremos no Uber com LSTM

Em breve teremos alguns posts aqui no blog sobre o assunto, mas é um case de ML com engenharia de caras questão mandando bem com métodos bem avançados com arquiteturas escaláveis.

ENGINEERING EXTREME EVENT FORECASTING AT UBER WITH RECURRENT NEURAL NETWORKS J – BY NIKOLAY LAPTEV, SLAWEK SMYL, & SANTHOSH SHANMUGAM

We ultimately settled on conducting time series modeling based on the Long Short Term Memory (LSTM) architecture, a technique that features end-to-end modeling, ease of incorporating external variables, and automatic feature extraction abilities.4 By providing a large amount of data across numerous dimensions, an LSTM approach can model complex nonlinear feature interactions.

We decided to build a neural network architecture that provides single-model, heterogeneous forecasting through an automatic feature extraction module.6 As Figure 4 demonstrates, the model first primes the network by automatic, ensemble-based feature extraction. After feature vectors are extracted, they are averaged using a standard ensemble technique. The final vector is then concatenated with the input to produce the final forecast.

During testing, we were able to achieve a 14.09 percent symmetric mean absolute percentage error (SMAPE) improvement over the base LSTM architecture and over 25 percent improvement over the classical time series model used in Argos, Uber’s real-time monitoring and root cause-exploration tool.

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Prevendo eventos extremos no Uber com LSTM

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