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Found 20 papers, 8 papers with code
Deep Non-Parametric Time Series Forecaster
This paper presents non-parametric baseline models for time series forecasting.
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimisation
We introduce ordered transfer hyperparameter optimisation (OTHPO), a version of transfer learning for hyperparameter optimisation (HPO) where the tasks follow a sequential order.
Optimizing Hyperparameters with Conformal Quantile Regression
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.
Criteria for Classifying Forecasting Methods
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.
Multi-Objective Model Selection for Time Series Forecasting
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.
Meta-Forecasting by combining Global Deep Representations with Local Adaptation
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.
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
A multi-objective perspective on jointly tuning hardware and hyperparameters
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.
A resource-efficient method for repeated HPO and NAS problems
In this work we consider the problem of repeated hyperparameter and neural architecture search (HNAS).
Symbol-Shift Equivariant Neural Networks
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).
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
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.
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches.
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
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.
A Quantile-based Approach for Hyperparameter Transfer Learning
In this work, we introduce a novel approach to achieve transfer learning across different \emph{datasets} as well as different \emph{objectives}.
A Copula approach for hyperparameter transfer learning
In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different metrics.
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
We present a scalable and robust Bayesian inference method for linear state space models.
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
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
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Bayesian Intermittent Demand Forecasting for Large Inventories
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.
