A Weight-adjusting Approach on an Ensemble of Classifiers for Time Series Forecasting

Li, Lin; Ngan, Chun-Kit

doi:10.1145/3325917.3325920

Cited by 2 publications

(3 citation statements)

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“…A Weight-adjusting Approach on an Ensemble of Classifiers for Time Series Forecasting [61]: is focused on forecasting time series using hybrid heterogeneous forecasting model including ARIMA model, SVM and ANN. The approach used in this paper is to take each model's weight based on their ability and history of predicting numerical values.…”

Section: Group 4: Hybrid Modelmentioning

confidence: 99%

“…According to Ameer et al [39] compared to a decision tree, gradient boosting and multilinear perception, random forest obtained better results by reducing overfitting and detecting peak values. Although, on the other hand, Li and Ngan [61] mentioned that random forest could have a challenge with fitting a wide variety of data distribution. Shaban et al [34] noted that M5P compared to SVM and ANN, generalised better, and SVM can manage high dimensional data better than ANN.…”

Section: Aq Met Yesmentioning

confidence: 99%

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Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

2020

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The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.

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Section: Group 4: Hybrid Modelmentioning

confidence: 99%