Search Results for author: Nikolay Laptev

Found 6 papers, 3 papers with code

NeuralProphet: Explainable Forecasting at Scale

1 code implementation29 Nov 2021 Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, Ram Rajagopal

NeuralProphet is a hybrid forecasting framework based on PyTorch and trained with standard deep learning methods, making it easy for developers to extend the framework.

Decision Making Time Series

AR-Net: A simple Auto-Regressive Neural Network for time-series

2 code implementations27 Nov 2019 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.

Time Series

Deep and Confident Prediction for Time Series at Uber

4 code implementations6 Sep 2017 Lingxue Zhu, Nikolay Laptev

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.

Anomaly Detection Probabilistic Time Series Forecasting +2

Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem

no code implementations16 May 2017 Jeya Balaji Balasubramanian, Akshay Soni, Yashar Mehdad, Nikolay Laptev

The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time.

Rank-to-engage: New Listwise Approaches to Maximize Engagement

no code implementations24 Feb 2017 Swayambhoo Jain, Akshay Soni, Nikolay Laptev, Yashar Mehdad

For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e. g., click-through-rate, average engagement time etc.)

Learning-To-Rank

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