Search Results for author: Christoph Bergmeir

Found 27 papers, 17 papers with code

Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices

no code implementations21 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.

Decision Making Time Series

LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data

no code implementations15 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.

Decision Making Time Series

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

LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts

no code implementations13 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.

Time Series

A Look at the Evaluation Setup of the M5 Forecasting Competition

no code implementations8 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.

Decision Making

Monash Time Series Forecasting Archive

1 code implementation14 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.

Time Series Time Series Forecasting

Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

1 code implementation5 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).

Environmental Sound Classification Model Compression +1

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

no code implementations20 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.

Transfer Learning

SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

1 code implementation19 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.

MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification

1 code implementation31 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.

General Classification Time Series +1

Ensembles of Localised Models for Time Series Forecasting

1 code implementation30 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.

Time Series Time Series Forecasting

Global Models for Time Series Forecasting: A Simulation Study

1 code implementation23 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.

Time Series Time Series Forecasting

Model selection in reconciling hierarchical time series

1 code implementation21 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.

Model Selection Time Series +1

A Strong Baseline for Weekly Time Series Forecasting

1 code implementation16 Oct 2020 Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb, Pablo Montero-Manso

In particular, our model can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset.

Time Series Time Series Forecasting

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

no code implementations6 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.

Data Augmentation Dynamic Time Warping +2

Simulation and Optimisation of Air Conditioning Systems using Machine Learning

no code implementations27 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.

Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification

1 code implementation27 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.

Classification General Classification

Time Series Extrinsic Regression

1 code implementation23 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.

Time Series Time Series Classification +2

Monash University, UEA, UCR Time Series Extrinsic Regression Archive

2 code implementations19 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.

Time Series Time Series Classification +2

Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand

no code implementations25 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.

Time Series

Machine learning applications in time series hierarchical forecasting

no code implementations1 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.

Time Series

LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

3 code implementations10 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.

Time Series

Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

3 code implementations2 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.

Time Series Time Series Forecasting

LoRMIkA: Local rule-based model interpretability with k-optimal associations

no code implementations11 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.

Decision Making

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

1 code implementation13 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.

Time Series

Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach

3 code implementations9 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.

Time Series Time Series Clustering +1

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