Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for Atmospheric PM2.5 Forecasting in Bangladesh

Shahriar, Shihab Ahmad; Kayes, Imrul; Hasan, Kamrul; Hasan, Mahmudul; Islam, Rashik; Awang, Norrimi Rosaida; Hamzah, Zulhazman; Rak, Aweng Eh; Salam, Mohammed Abdus

doi:10.3390/atmos12010100

Cited by 53 publications

(25 citation statements)

References 51 publications

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“…The machine-learning model is one of the popular prediction methods in time series data, such that Fi-John Chang et al [42] used the self-organizing mapping method to extract spatiotemporal features of series data; Gholamreza Goudarzi et al [43] used an artificial neural network, and Wenbo Liu et al [44] used support vector regression with different kernel functions to predict the daily average concentration of PM2.5 in Beijing; Shihab Ahmad Shahriar et al [45] evaluated autoregressive integrated moving average (ARIMA)-artificial neural network (ANN), ARIMA-support vector machine (SVM), and principal component regression (PCR) along with decision tree (DT) and CatBoost deep learning model for the prediction. In practice, machine-learning methods are unsuitable for big time series data prediction with complex nonlinearities because they will fail to give enough performance.…”

Section: Related Workmentioning

confidence: 99%

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

et al. 2022

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Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting. This paper proposes a novel planar flow-based variational auto-encoder prediction model (PFVAE), which uses the long- and short-term memory network (LSTM) as the auto-encoder and designs the variational auto-encoder (VAE) as a time series data predictor to overcome the noise effects. In addition, the internal structure of VAE is transformed using planar flow, which enables it to learn and fit the nonlinearity of time series data and improve the dynamic adaptability of the network. The prediction experiments verify that the proposed model is superior to other models regarding prediction accuracy and proves it is effective for predicting time series data.

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Section: Related Workmentioning

confidence: 99%

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

et al. 2022

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“…The models were trained both with and without incorporating the cost of the structure into the training set. As the ensemble learning methods, Random Forest (RF), LightGBM, XGBoost, and CatBoost are implemented since these four algorithms are reported as the best-performing algorithms in the recent research literature [11][12][13][14][15]. The harmony search procedure is used in database generation and elaborated in the following section.…”

Section: Optimization and Machine Learning Methodologiesmentioning

confidence: 99%

Optimum Design of Cylindrical Walls Using Ensemble Learning Methods

et al. 2022

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The optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.

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“…For operating the ARIMA model by the Box-Jenkins methodology, there are three steps that should be considered : identification, estimation of parameters and forecasting [26]. The identification step is done by checking whether the dataset used is stationary.…”

Section: F Auto Regressive Integrated Moving Averagementioning

confidence: 99%

Comparative Study of Forecasting Models for Forex Predictions with the Impact of Different Currencies

Agung¹,

Rianto²,

Wibowo³

2022

IJETAE

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- Foreign Exchange (Forex) is the exchange / trading of currencies from different countries with the aim of making profit. Exchange rates on Forex markets are always changing and it is hard to predict. Many factors affect exchange rates of certain currency pairs like inflation rates, interest rates, government debt, term of trade, political stability of certain countries, recession and many more. Uncertainty in Forex prediction can be reduced with the help of technology by using machine learning. There are many machine learning methods that can be used when predicting Forex. The methods used in this paper are Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR). XGBOOST, and ARIMA. The outcome of this paper will be comparison results that show how other major currency pairs have influenced the performance and accuracy of different methods. From the results, it was proven that XGBoost outperformed other models by 0.36% compared to ARIMA model, 4.4% compared to GRU model, 8% compared to LSTM model, 9.74% compared to SVR model. Keywords— Forex Forecasting, Long Short Term Memory, Gated Recurrent Unit, Support Vector Regression, ARIMA, Extreme Gradient Boosting

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Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for Atmospheric PM2.5 Forecasting in Bangladesh

Cited by 53 publications

References 51 publications

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data

Optimum Design of Cylindrical Walls Using Ensemble Learning Methods

Comparative Study of Forecasting Models for Forex Predictions with the Impact of Different Currencies

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