Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

Accurate time series forecasting during high variance segments (e.g., holidays and sporting events) is critical for anomaly detection, resource allocation, budget planning, and other related tasks necessary to facilitate optimal Uber user experiences at scale. Forecasting these variables, however, can be challenging because extreme event prediction depends on weather, city population growth, and other external factors that contribute to forecast uncertainty. In recent years, the Long Short Term Memory (LSTM) technique has become a popular time series modeling framework due to its end-to-end modeling, ease of incorporating exogenous variables, and automatic feature extraction abilities. Uncertainty estimation in deep learning remains a less trodden but increasingly important component of assessing forecast prediction truth in LSTM models. Through our research, we found that a neural network forecasting model is able to outperform classical time series methods in use cases with long, interdependent time series. While beneficial in other ways, our new model did not offer insights into prediction uncertainty, which helps determine how much we can trust the forecast.