Ensemble Methods for Time Series Forecasting

Allende, Héctor; Valle, Carlos

doi:10.1007/978-3-319-48317-7_13

Cited by 14 publications

(15 citation statements)

References 59 publications

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“…Regarding future expansions of this work, one can experiment with the adoption of metrics that can select models with complementary predictive behavior and could operate as an asset for improving the performance of the proposed model [2]. In many cases, energy forecasting predictions are needed for multiple steps in the future.…”

Section: Discussionmentioning

confidence: 99%

“…These findings support the powerful modeling capabilities of tree-based ensemble regression. Instead of searching exhaustively for fixed-size combinations, the concept of negative correlation was employed in [2] for selecting a subset of models among a predefined pool of candidates, without violating their bias-variance trade-off, but taking into consideration the covariance measure.…”

Section: Related Workmentioning

confidence: 99%

“…Classical ML regression has been extensively applied to the task in the last years aiming to provide better results for both short-term and long-term scenarios, offering also increased robustness compared to pure statistical approaches. Following this direction, several types of ensemble methods have been recently considered in the context of time series forecasting, as they are described in [2] and thoroughly investigated in [3].…”

Section: Introductionmentioning

confidence: 99%

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Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles

Vartholomaios¹,

Karlos²,

Kouloumpris³

et al. 2021

Preprint

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Energy production using renewable sources exhibits inherent uncertainties due to their intermittent nature. Nevertheless, the unified European energy market promotes the increasing penetration of renewable energy sources (RES) by the regional energy system operators. Consequently, RES forecasting can assist in the integration of these volatile energy sources, since it leads to higher reliability and reduced ancillary operational costs for power systems. This paper presents a new dataset for solar and wind energy generation forecast in Greece and introduces a feature engineering pipeline that enriches the dimensional space of the dataset. In addition, we propose a novel method that utilizes the innovative Prophet model, an end-to-end forecasting tool that considers several kinds of nonlinear trends in decomposing the energy time series before a tree-based ensemble provides short-term predictions. The performance of the system is measured through representative evaluation metrics, and by estimating the model's generalization under an industryprovided scheme of absolute error thresholds. The proposed hybrid model competes with baseline persistence models, tree-based regression ensembles, and the Prophet model, managing to outperform them, presenting both lower error rates and more favorable error distribution.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles

Vartholomaios¹,

Karlos²,

Kouloumpris³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The ensemble method is one of the most successful approaches for performing time series forecasting tasks [36,37]. It refers to a method that combines multiple predictive learning algorithms to obtain results superior to those that could be obtained from a single algorithm.…”

Section: The Stacking Ensemble Learning Methods (Selm)mentioning

confidence: 99%

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Lee

Kim

2019

Applied Sciences

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Electric load forecasting for buildings is important as it assists building managers or system operators to plan energy usage and strategize accordingly. Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting. Self-organizing map (SOM) has been successfully utilized to cluster a dataset into subsets containing similar data points. These subsets are then used to train the forecasting models to improve forecasting accuracy. However, some buildings may have insufficient data since newly installed monitoring devices such as AMI have no choice but to collect a limited amount of data. Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. This results in a relatively high generalization error. In this study, we propose to address this problem by employing the stacking ensemble learning method (SELM) that is well-known for its generalization ability. An experimental study was conducted using the electricity consumption data of an actual institutional building and meteorological data. Our proposed model outperformed other baseline models, which means it successfully mitigates the effect of overfitting.

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“…Ensembles are statistical learning models that combine individual models, with the goal of capturing the strengths of each individual in a diverse set 93,94 . Theoretically and empirically, ensembles have often outperformed individual models for general prediction tasks 93 , as well as time series prediction 95 . Common approaches involve perturbing the data (e.g., through bagging), altering the individual models (e.g., by adding regularization terms), or aggregating the outputs from individual models (e.g., by computing the mean) 95 .…”

Section: Combining Predictive Modelsmentioning

confidence: 99%

Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil

Kempfert,

Martinez,

Siraj

et al. 2020

Preprint

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Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. Novel data streams, such as Internet and social media data, have recently been reported to benefit infectious disease prediction. However, few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatiotemporal resolutions. As a case study of dengue in Brazil, we have combined multiple traditional and non-traditional, heterogeneous data streams (satellite imagery, Internet, weather, and clinical surveillance data) across its 27 states on a weekly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data (seasonal autoregressive integrated moving average, vector autoregression, seasonal trend decomposition based on locally estimated scatterplot smoothing, and their variants). The top-performing model varies by state, motivating our consideration of ensemble approaches to automatically combine these models for better outcomes at the state level. For a trimmed mean ensemble approach, 25 states achieve Pearson correlation coefficients (between fitted and observed values in the 2015-16 testing window) exceeding 80%; meanwhile, the median value over the 27 states is 91.75%, and the maximum is 96.44%. Associated 95% prediction intervals reach approximately 96% empirical coverage or more for half the states. Model comparisons suggest that predictions often improve with the addition of exogenous data, although similar performance can be attained by including only one exogenous data stream (either weather data or the novel satellite data) rather than combining all of them. Among the Brazilian states, the model performance is spatially autocorrelated and associated with measures involving education, employment, and population. Our results demonstrate that Brazil can be nowcasted at the state level with high accuracy and confidence, inform the utility of each individual data stream, and reveal potential geographic contributors to predictive performance. Our work can be extended to other spatial levels of Brazil, vector-borne diseases, and countries, so that the spread of infectious disease can be more effectively curbed.

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Ensemble Methods for Time Series Forecasting

Cited by 14 publications

References 59 publications

Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles

Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based Ensembles

Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil

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