no code implementations • 12 May 2024 • Abishek Sriramulu, Christoph Bergmeir, Slawek Smyl
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation.
1 code implementation • 6 Dec 2023 • Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
In this paper, we propose a hybrid approach combining neural networks and statistical structure learning models to self-learn the dependencies and construct a dynamically changing dependency graph from multivariate data aiming to enable the use of GNNs for multivariate forecasting even when a well-defined graph does not exist.
no code implementations • 2 Dec 2023 • Aidan Quinn, Melanie Simmons, Benjamin Spivak, Christoph Bergmeir
Psychometric assessment instruments aid clinicians by providing methods of assessing the future risk of adverse events such as aggression.
no code implementations • 7 Nov 2023 • Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara, Christoph Bergmeir, Juan Bernabe-Moreno, Robert Eigenmann, Nils Einecke, Selvi Ergen, Rakshitha Godahewa, Hansika Hewamalage, Jesus Lago, Steffen Limmer, Sven Rebhan, Boris Rabinovich, Dilini Rajapasksha, Heda Song, Christian Wagner, Wenlong Wu, Luis Magdalena, Isaac Triguero
These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors.
no code implementations • 2 Nov 2023 • Xueying Long, Quang Bui, Grady Oktavian, Daniel F. Schmidt, Christoph Bergmeir, Rakshitha Godahewa, Seong Per Lee, Kaifeng Zhao, Paul Condylis
We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets.
no code implementations • 26 Oct 2023 • Rakshitha Godahewa, Christoph Bergmeir, Zeynep Erkin Baz, Chengjun Zhu, Zhangdi Song, Salvador García, Dario Benavides
To fill this gap, we propose a simple linear-interpolation-based approach that is applicable to stabilise the forecasts provided by any base model vertically and horizontally.
no code implementations • 25 Sep 2023 • Slawek Smyl, Christoph Bergmeir, Alexander Dokumentov, Xueying Long, Erwin Wibowo, Daniel Schmidt
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models, to model series that grow faster than linear but slower than exponential.
no code implementations • 25 Aug 2023 • Md Mohaimenuzzaman, Christoph Bergmeir, Bernd Meyer
The scarcity of labelled data makes training Deep Neural Network (DNN) models in bioacoustic applications challenging.
1 code implementation • 4 Apr 2023 • Ziyi Liu, Rakshitha Godahewa, Kasun Bandara, Christoph Bergmeir
Handling concept drift in forecasting is essential for many ML methods in use nowadays, however, the prior work only proposes methods to handle concept drift in the classification domain.
no code implementations • 21 Dec 2022 • Christoph Bergmeir, Frits de Nijs, Abishek Sriramulu, Mahdi Abolghasemi, Richard Bean, John Betts, Quang Bui, Nam Trong Dinh, Nils Einecke, Rasul Esmaeilbeigi, Scott Ferraro, Priya Galketiya, Evgenii Genov, Robert Glasgow, Rakshitha Godahewa, Yanfei Kang, Steffen Limmer, Luis Magdalena, Pablo Montero-Manso, Daniel Peralta, Yogesh Pipada Sunil Kumar, Alejandro Rosales-Pérez, Julian Ruddick, Akylas Stratigakos, Peter Stuckey, Guido Tack, Isaac Triguero, Rui Yuan
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
1 code implementation • 16 Nov 2022 • Rakshitha Godahewa, Geoffrey I. Webb, Daniel Schmidt, Christoph Bergmeir
On the other hand, in the forecasting community, general-purpose tree-based regression algorithms (forests, gradient-boosting) have become popular recently due to their ease of use and accuracy.
1 code implementation • 14 Nov 2022 • Jahan C. Penny-Dimri, Christoph Bergmeir, Julian Smith
Most practical data science problems encounter missing data.
no code implementations • 19 Sep 2022 • Ankitha Nandipura Prasanna, Priscila Grecov, Angela Dieyu Weng, Christoph Bergmeir
We consider the impact of Covid-19 lockdowns on energy usage as a case study to evaluate the non-uniform effect of this intervention on the electricity demand distribution.
no code implementations • 15 Sep 2022 • Alexey Chernikov, Chang Wei Tan, Pablo Montero-Manso, Christoph Bergmeir
Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work.
no code implementations • 21 Mar 2022 • Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir
We elaborate on the different problematic characteristics of time series such as non-normalities and non-stationarities and how they are associated with common pitfalls in forecast evaluation.
no code implementations • 15 Feb 2022 • Dilini Rajapaksha, Christoph Bergmeir
In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way.
1 code implementation • 29 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.
no code implementations • 13 Nov 2021 • Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman
Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches.
1 code implementation • 13 Aug 2021 • Md Mohaimenuzzaman, Christoph Bergmeir, Bernd Meyer
These technologies must ultimately be brought directly to the edge to fully harness the power of deep learning for the IoT.
no code implementations • 8 Aug 2021 • Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir, Rob J Hyndman
Scale normalization of the M5 error measure results in less stability than other scale-free errors.
1 code implementation • 14 May 2021 • Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Rob J. Hyndman, Pablo Montero-Manso
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area.
1 code implementation • 5 Mar 2021 • Md Mohaimenuzzaman, Christoph Bergmeir, Ian Thomas West, Bernd Meyer
Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support).
Ranked #18 on Audio Classification on ESC-50
no code implementations • 20 Feb 2021 • Seyedali Meghdadi, Guido Tack, Ariel Liebman, Nicolas Langrené, Christoph Bergmeir
To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics.
1 code implementation • 19 Feb 2021 • Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, Wray Buntine
Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i. e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.
1 code implementation • 31 Jan 2021 • Chang Wei Tan, Angus Dempster, Christoph Bergmeir, Geoffrey I. Webb
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art performance with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods.
1 code implementation • 30 Dec 2020 • Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb, Slawek Smyl, Christoph Bergmeir
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly outperforming traditional univariate forecasting models that work on isolated series.
1 code implementation • 23 Dec 2020 • Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Our experiments demonstrate that when trained as global forecasting models, techniques such as RNNs and LGBMs, which have complex non-linear modelling capabilities, are competitive methods in general under challenging forecasting scenarios such as series having short lengths, datasets with heterogeneous series and having minimal prior knowledge of the patterns of the series.
1 code implementation • 21 Oct 2020 • Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis, Christoph Bergmeir
However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent.
1 code implementation • 16 Oct 2020 • Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso
Many businesses and industries require accurate forecasts for weekly time series nowadays.
no code implementations • 6 Aug 2020 • Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques.
1 code implementation • 27 Jun 2020 • Rakshitha Godahewa, Trevor Yann, Christoph Bergmeir, Francois Petitjean
This paper explores how to best handle seasonal drift in the specific context of news article categorization (or classification/tagging), where seasonal drift is overwhelmingly the main type of drift present in the data, and for which the data are high-dimensional.
no code implementations • 27 Jun 2020 • Rakshitha Godahewa, Chang Deng, Arnaud Prouzeau, Christoph Bergmeir
In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy.
1 code implementation • 23 Jun 2020 • Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb
This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label.
2 code implementations • 19 Jun 2020 • Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb
We refer to this problem as Time Series Extrinsic Regression (TSER), where we are interested in a more general methodology of predicting a single continuous value, from univariate or multivariate time series.
no code implementations • 25 Apr 2020 • Kasun Bandara, Christoph Bergmeir, Sam Campbell, Deborah Scott, Dan Lubman
Emergency Medical Services (EMS) demand load has become a considerable burden for many government authorities, and EMS demand is often an early indicator for stress in communities, a warning sign of emerging problems.
no code implementations • 1 Dec 2019 • Mahdi Abolghasemi, Rob J. Hyndman, Garth Tarr, Christoph Bergmeir
We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.
3 code implementations • 10 Sep 2019 • Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays.
3 code implementations • 2 Sep 2019 • Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition.
no code implementations • 11 Aug 2019 • Dilini Rajapaksha, Christoph Bergmeir, Wray Buntine
In this paper, we propose Local Rule-based Model Interpretability with k-optimal Associations (LoRMIkA), a novel model-agnostic approach that obtains k-optimal association rules from a neighbourhood of the instance to be explained.
1 code implementation • 13 Jan 2019 • Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Quoc Tran, Brian Seaman
Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model.
3 code implementations • 9 Oct 2017 • Kasun Bandara, Christoph Bergmeir, Slawek Smyl
In particular, in terms of mean sMAPE accuracy, it consistently outperforms the baseline LSTM model and outperforms all other methods on the CIF2016 forecasting competition dataset.