A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning

Gastinger, Julia; Nicolas, Sébastien; Stepić, Dušica; Schmidt, Mischa; Schülke, Anett

doi:10.1109/ijcnn52387.2021.9533378

Cited by 18 publications

(5 citation statements)

References 17 publications

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“…providing better predictions, as verified by all the reported measures. The fact that the fused method (ensemble) performs better than the individual methods is a recurring pattern in the ensemble learning literature [54]. Combining different predictors increases the confidence in the predictions and reduces the error.…”

Section: Plos Onementioning

confidence: 99%

A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings

Kalamaras,

Sánchez-Corcuera,

Casado-Mansilla

et al. 2024

PLoS ONE

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Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees’ decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.

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Section: Plos Onementioning

confidence: 99%

A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings

Kalamaras,

Sánchez-Corcuera,

Casado-Mansilla

et al. 2024

PLoS ONE

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“…For a similar forecasting challenge, [46] combined four base models-random forests, long short-term memory (LSTM), deep neural networks, and evolutionary trees-with gradient boosting and extreme gradient boosting as metalearners, resulting in a significant reduction in forecast error. In [20], the authors conducted a comparative analysis of various forecast combination strategies, including simple averaging, linear combination with weights based on performance, FFORMA, and stacking. Experimental results involving 16,000 time series from diverse sources demonstrated that stacking outperforms its counterparts, underscoring its efficacy for time series with varying characteristics.…”

Section: Combining Forecastsmentioning

confidence: 99%

“…When dealing with constituent forecasts derived from nonlinear models or situations where the true relationship between combination members and the best forecast involves nonlinear systems, ML models can be harnessed to nonlinearly combine the base forecasts through a stacking procedure [5,20]. Stacking, in this context, can enhance forecast accuracy by learning the optimal combination of constituent forecasts in a data-driven manner.…”

Section: Combining Forecastsmentioning

confidence: 99%

Ensemble Learning by Stacking for Deterministic and Probabilistic Short-term Load Forecasting

Dudek

2023

Preprint

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In this study, we explore meta-learning for combining forecasts derived from individual base models for both deterministic and probabilistic short-term load forecasting. Unlike conventional approaches that rely on straightforward averaging, we harness the power of machine learning to implement more advanced methods for combining forecasts through meta-learning, consequently increasing the accuracy of forecasts. For deterministic forecasting, we employ a spectrum of meta-learners, including linear regression, k-nearest neighbors, multilayer perceptron, random forest, and long short-term memory. For probabilistic forecasting, we utilize quantile linear regression, quantile regression forest and post-processing involving residual simulation. We introduce both global and local meta-learning variants tailored for time series data characterized by intricate seasonality patterns. Through a comprehensive assessment across various forecasting scenarios, we illustrate the superior performance of meta-learners in comparison to conventional averaging techniques.

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“…Neural networks (NNs) are often used in stacking to estimate the nonlinear mapping between the target value and its forecasts produced by multiple models [11]. The power of ensemble learning for forecasting was demonstrated in [12], where several meta-learning approaches were evaluated on a large and diverse set of time series data. Ensemble methods were found to provide a benefit in overall forecasting accuracy, with simple ensemble methods leading to good results on average.…”

Section: Introductionmentioning

confidence: 99%

Combining Forecasts of Time Series with Complex Seasonality Using LSTM-Based Meta-Learning

Dudek

2023

Itise 2023

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A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning

Cited by 18 publications

References 17 publications

A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings

A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings

Ensemble Learning by Stacking for Deterministic and Probabilistic Short-term Load Forecasting

Combining Forecasts of Time Series with Complex Seasonality Using LSTM-Based Meta-Learning

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