no code implementations • 22 Dec 2023 • Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, Tim Januschowski
This paper presents non-parametric baseline models for time series forecasting.
3 code implementations • 6 Nov 2023 • David Salinas, Nick Erickson
We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
1 code implementation • 29 Jun 2023 • Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas, Aaron Klein
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order.
1 code implementation • 5 May 2023 • David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search.
no code implementations • 7 Dec 2022 • Tim Januschowski, Jan Gasthaus, Yuyang Wang, David Salinas, Valentin Flunkert, Michael Bohlke-Schneider, Laurent Callot
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers.
no code implementations • 17 Feb 2022 • Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection.
no code implementations • 5 Nov 2021 • Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias Seeger, Cedric Archambeau
While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy.
2 code implementations • 23 Jun 2021 • Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
no code implementations • 10 Jun 2021 • David Salinas, Valerio Perrone, Olivier Cruchant, Cedric Archambeau
In three benchmarks where hardware is selected in addition to hyperparameters, we obtain runtime and cost reductions of at least 5. 8x and 8. 8x, respectively.
no code implementations • ICML Workshop AutoML 2021 • Giovanni Zappella, David Salinas, Cédric Archambeau
In this work we consider the problem of repeated hyperparameter and neural architecture search (HNAS).
no code implementations • 1 Jan 2021 • David Salinas, Hady Elsahar
Neural networks have been shown to have poor compositionality abilities: while they can produce sophisticated output given sufficient data, they perform patchy generalization and fail to generalize to new symbols (e. g. switching a name in a sentence by a less frequent one or one not seen yet).
no code implementations • 20 May 2020 • Stephan Rabanser, Tim Januschowski, Valentin Flunkert, David Salinas, Jan Gasthaus
In particular, we investigate the effectiveness of several forms of data binning, i. e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models.
1 code implementation • 21 Apr 2020 • Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Francois-Xavier Aubet, Laurent Callot, Tim Januschowski
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
2 code implementations • NeurIPS 2019 • David Salinas, Michael Bohlke-Schneider, Laurent Callot, Roberto Medico, Jan Gasthaus
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or demand forecasting.
no code implementations • ICML 2020 • David Salinas, Huibin Shen, Valerio Perrone
In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}.
no code implementations • 25 Sep 2019 • David Salinas, Huibin Shen, Valerio Perrone
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics.
6 code implementations • 12 Jun 2019 • Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
no code implementations • 22 Sep 2017 • Matthias Seeger, Syama Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
We present a scalable and robust Bayesian inference method for linear state space models.
19 code implementations • 13 Apr 2017 • David Salinas, Valentin Flunkert, Jan Gasthaus
Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1
no code implementations • NeurIPS 2016 • Matthias W. Seeger, David Salinas, Valentin Flunkert
We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics.