Explainable boosted linear regression for time series forecasting

Ilic, Igor; Gorgulu, Berk; Çevik, Mücahit; Baydoğan, Mustafa Gökçe

doi:10.1016/j.patcog.2021.108144

Cited by 47 publications

(26 citation statements)

References 26 publications

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“…To accurately test our proposed EXPECT approach, we led a comparison with the existing consumption prediction baseline methods; the statistical linear regression model proposed in [5] and the machine learning-based Random forest model implemented in [16]. In addition, for the sake of a fair comparison, we tested all the baseline methods using the same datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Afterwards, the XGboost model processes the result as a feature with other energy-related factors. Recently, Ilic et al proposed an explainable boosted linear regression (EBLR) algorithm for time-series forecasting [16]. Their iterative model operates into two phases: In the first phase, a base model, such as linear regression, is trained to obtain the preliminary forecasts.…”

Section: Hybrid Approachesmentioning

confidence: 99%

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Expect: EXplainable Prediction Model for Energy ConsumpTion

et al. 2022

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With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

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Section: Resultsmentioning

confidence: 99%

Section: Hybrid Approachesmentioning

confidence: 99%

Expect: EXplainable Prediction Model for Energy ConsumpTion

et al. 2022

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“…Most algorithms do not fit a specific dataset, which is why selecting the right method for each dataset is critical [24]. Some of the predictive algorithms only results into a binomial result which are 1 or 0, true or false and yes or no, but some have multiple output which are called multivariate results which are person, cat, dog.…”

Section: Figure 2 Methodologymentioning

confidence: 99%

Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

Piol¹,

Lacatan²,

Pulumbarit³

2022

IJETAE

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The use of Linear Regression in predicting enrolment has been shown to be beneficial, although it varies with various datasets and attributes; varying weights of the correlation of the attributes can be discarded if they do not impact the prediction. Data collecting had grown since prior investigations, resulting in a more complicated dataset with many varieties. As a result of the data being created by multiple clerks, cleaning and combining proved tough; nonetheless, the fundamental parameters remain intact. Different algorithms were examined but Linear Regression obtained the highest accuracy with a 12.398 percentage for the absolute error and a root mean squared of 26.936 to create a tangible model to anticipate the enrolment of Region IVA CALABARZON in the Philippines. This demonstrates that it was 2.067 percentage points more than the prior research.

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“…L INEAR regression is one of the fundamental models in machine learning. The advancement and broad application of linear regression have brought great convenience and improvement to human development in decision support systems [49], time series forecasting [26], climate prediction [33], smart grid [34], and signal processing [22], to name a few. A traditional linear regression model is usually performed in a centralized way, where data is gathered and processed on a central server.…”

Section: Introductionmentioning

confidence: 99%

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

Leng¹,

Li²,

Wang³

2022

Preprint

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Distributed privacy-preserving regression schemes have been developed and extended in various fields, where multiparty collaboratively and privately run optimization algorithms, e.g., Gradient Descent, to learn a set of optimal parameters. However, traditional Gradient-Descent based methods fail to solve problems which contains objective functions with L 1 regularization, such as Lasso regression. In this paper, we present Federated Coordinate Descent, a new distributed scheme called FCD, to address this issue securely under multiparty scenarios. Specifically, through secure aggregation and added perturbations, our scheme guarantees that: (1) no local information is leaked to other parties, and (2) global model parameters are not exposed to cloud servers. The added perturbations can eventually be eliminated by each party to derive a global model with high performance. We show that the FCD scheme fills the gap of multiparty secure Coordinate Descent methods and is applicable for general linear regressions, including linear, ridge and lasso regressions. Theoretical security analysis and experimental results demonstrate that FCD can be performed effectively and efficiently, and provide as low MAE measure as centralized methods under tasks of three types of linear regressions on real-world UCI datasets.

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Explainable boosted linear regression for time series forecasting

Cited by 47 publications

References 26 publications

Expect: EXplainable Prediction Model for Energy ConsumpTion

Expect: EXplainable Prediction Model for Energy ConsumpTion

Performance Evaluation of Regression Analysis Algorithms in Predicting the Enrollment of Basic Education

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

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