353 citations · 444 across the 9 of their papers we have counts for
9 papers
NeuralProphet: Explainable Forecasting at Scale
Oskar Triebe, Hansika Hewamalage, Polina Pilyugina +3
We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferat…
Self-supervised learning for fast and scalable time series hyper-parameter tuning
Peiyi Zhang, Xiaodong Jiang, Ginger M Holt +4
Hyper-parameters of time series models play an important role in time series analysis. Slight differences in hyper-parameters might lead to very different forecast results for a gi…
Scalable Data Classification for Security and Privacy
Paulo Tanaka, Sameet Sapra, Nikolay Laptev
Content based data classification is an open challenge. Traditional Data Loss Prevention (DLP)-like systems solve this problem by fingerprinting the data in question and monitoring…
AR-Net: A simple Auto-Regressive Neural Network for time-series
Oskar Triebe, Nikolay Laptev, Ram Rajagopal
In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-ra…
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment
Fred Lin, Keyur Muzumdar, Nikolay Pavlovich Laptev +3
Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a…
Deep and Confident Prediction for Time Series at Uber
Lingxue Zhu, Nikolay Laptev
Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting…