Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators

Chen, Ken; Peng, Yaqi; Lu, Shengyong; Lin, Binbin

doi:10.1016/j.chemosphere.2021.129802

Cited by 30 publications

(11 citation statements)

References 39 publications

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“…One can conclude that combination of models has got forecast of the same quality or even better. This conclusion is confirmed in [4][5][6][7] and in this paper. But narrowing of prediction intervals for combination of models is still investigated [12,13] and is in the scope of further research.…”

Section: Discussionsupporting

confidence: 89%

“…At the same time there's idea of averaging forecasts made by means of a few models. If all the models make good forecasts it's obvious that combination of their forecasts should behave well also [4][5][6][7]10]. But this result needs more thorough check and strict confirmation.…”

Section: Time Series Forecast Errors and Prediction Intervalsmentioning

confidence: 99%

“…Prediction intervals should also be evaluated as well as prediction quality. Implementation of non-linear combination forecasters [12] is an idea of next stage of research in this area comparing to linear combination technique used in [4][5][6][7]. Competitive selection of best models by their prediction intervals quality is under investigation of [13].…”

Section: Time Series Forecast Errors and Prediction Intervalsmentioning

confidence: 99%

“…At the same time it's possible to make averaging of forecasts made with each model. Such technique is investigated in [4][5][6][7] and it has got good results. But this approach isn't confirmed well theoretically.…”

Section: Introductionmentioning

confidence: 99%

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Improvement of time series forecasting quality by means of multiple models prediction averaging

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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Construction of time series models is usually based on Akaike and Bayes information criteria. Requirement of model simplicity is built inside the information criteria structure and the best fitted models aren't always best in terms of criteria values. Often there are a few best models that fit investigated time series well and there's problem of choice between them. Usually information criteria values allow to choose among them. But if one needs the best model by forecasts quality, best fitted models, or there are other thoughts a research has to choose and test models manually. At the same time when a few models are chosen it's possible to construct their combination. The simplest way is to count mean value of their forecasts and to use it as a combined prediction. Practical researches confirm that forecast error gets lower in this approach. Also, more complex construction than averaging of forecasts can be used (for example, weighted voting that is widely used in bagging technique in solution of classification problems). But this approach hasn't got enough theoretical base. From theoretical point of view confidence intervals of time series forecasts (usually here they're considered as prediction intervals) is also very complex task. Intervals tend to be very wide even for models with good prediction quality in terms of mean forecast errors. Thus, prediction intervals are rare in use. In this paper a few time series models are constructed for wage and income indices of Russian macroeconomic time series. Their predictions are combined into one forecast and it's quality is compared to individual ones. Transformation of forecast variance and prediction intervals in case of simple (moving averages MA(q) and autoregressions AR(p) of low order) models is also considered but is a part of further work.

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Section: Discussionsupporting

confidence: 89%

Section: Time Series Forecast Errors and Prediction Intervalsmentioning

confidence: 99%

Section: Time Series Forecast Errors and Prediction Intervalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improvement of time series forecasting quality by means of multiple models prediction averaging

2021

Proceedings of the III International Workshop on Modeling, Information Processing and Computing (MIP: Computing-2021)

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“…Though, mathematical statement of this problem isn't researched well. There are attempts to choose the best model [1], to construct mean model [2], to implement bagging strategy to processed models of certain time series [3,4]. Selection of the best model is traditional way but in practice there's always a lot of models and there's no mathematically strict way to choose the best one.…”

Section: Introductionmentioning

confidence: 99%

Modeling of forecasts variance reduction at multiple time series prediction averaging with ARMA (1, q) functions

Musatov¹,

Petrusevich²

2022

Proceedings of the v International Workshop on Modeling, Information Processing and Computing (MIP: Computing-v 2022)

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Combination of time series forecasts is usually considered as good technique in practice. But it has got weak theoretical explanation. In this research variance of time series forecasts and variance of combined models are considered. One is interested in the view of variance of forecasts function over one, two and three periods. Conditions which can lead to improvement of averaged time series predictions are in scope of this research. In this paper a few examples of the most popular time series models are observed: the moving average models MA(q), the autoregressions AR(p) models and their combination in the form of ARIMA(p, d, q) or ARMA(p, q) model. In particular, AR(1) and ARMA(1, q) are investigated. Nowadays there are researches about time series averaging. Approaches based on bagging and boosting are implemented very often in classification and regressions. It’s very appealing to use such strategy in tine series modeling. At the same time it’s easy to construct learning set and test set in classification tasks. But it’s a complex task in case of time series processing. There’s a need of two sets: to train time series models and to construct their combinations. Thus nowadays combination of time series models, combination of their forecasts or of their prediction intervals are in scope of view of a few complex researches. In this paper we investigate behaviour of time series predictions’ variance in order to have another useful approach in time series prediction averaging. Russian macroeconomical time series statistics is used as experimental time series.

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Predicting wetland area and water depth in Barind plain of India

Singha

Pal

2022

Environ Sci Pollut Res

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Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators

Cited by 30 publications

References 39 publications

Improvement of time series forecasting quality by means of multiple models prediction averaging

Improvement of time series forecasting quality by means of multiple models prediction averaging

Modeling of forecasts variance reduction at multiple time series prediction averaging with ARMA (1, q) functions

Predicting wetland area and water depth in Barind plain of India

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