- Home
- /
- Nantachai Kantanantha
Asst. Prof. Nantachai Kantanantha, Ph.D.
- 5th Floor of Engineering 4 Bldg., Room 508
- +66-2218-6820
- nantachai.k@chula.ac.th
Education
Ph.D. in Industrial Engineering
Georgia Institute of Technology, United States, 2007
M.S. in Industrial Engineering
Georgia Institute of Technology, United States, 2001
B.Eng. in Industrial Engineering
Chulalongkorn University, Thailand
Expertise
Statistics & Data Analysis
Publications
2024
Lugnapis Kaewwhangsakul, Nantachai Kantanantha
Exploring the Characteristics of Time Series Data Affecting Forecasting Errors Using LSTM Conference
Association for Computing Machinery, 2024, ISBN: 979-840071820-5, (Cited by: 0).
@conference{Kaewwhangsakul20248,
title = {Exploring the Characteristics of Time Series Data Affecting Forecasting Errors Using LSTM},
author = {Lugnapis Kaewwhangsakul and Nantachai Kantanantha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207097394&doi=10.1145%2f3695220.3695224&partnerID=40&md5=69cbea1076542f9f0e9401cca11b0a4d},
doi = {10.1145/3695220.3695224},
isbn = {979-840071820-5},
year = {2024},
date = {2024-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {8 – 13},
publisher = {Association for Computing Machinery},
abstract = {This paper investigates the predictive capabilities of Long Short-Term Memory (LSTM) models using diverse time series data relevant to lifestyle decisions and business planning, such as stock prices, exchange rates, gold prices, and weather conditions. The main contributions include analyzing 42 datasets from Kaggle.com to identify influential features impacting LSTM prediction accuracy. Findings reveal varying prediction accuracy across datasets due to different data characteristics, with some datasets exhibiting high Mean Absolute Percentage Error (MAPE) when they process Coefficient of Variation (CV) exceeding 50%. Additionally, datasets with non-normal distributions and high kurtosis show greater prediction errors. These findings underscore the significance of considering dataset features for accurate LSTM predictions in real-world applications. © 2024 Owner/Author.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper investigates the predictive capabilities of Long Short-Term Memory (LSTM) models using diverse time series data relevant to lifestyle decisions and business planning, such as stock prices, exchange rates, gold prices, and weather conditions. The main contributions include analyzing 42 datasets from Kaggle.com to identify influential features impacting LSTM prediction accuracy. Findings reveal varying prediction accuracy across datasets due to different data characteristics, with some datasets exhibiting high Mean Absolute Percentage Error (MAPE) when they process Coefficient of Variation (CV) exceeding 50%. Additionally, datasets with non-normal distributions and high kurtosis show greater prediction errors. These findings underscore the significance of considering dataset features for accurate LSTM predictions in real-world applications. © 2024 Owner/Author.
2023
Nantachai Kantanantha
Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms Conference
vol. 35, IOS Press BV, 2023, ISSN: 2352751X, (Cited by: 0; All Open Access, Gold Open Access).
@conference{Kantanantha2023248,
title = {Forecasting of Thailand Electricity Load Consumption Using Machine Learning Algorithms},
author = {Nantachai Kantanantha},
editor = {Tang L.-C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173555567&doi=10.3233%2fATDE230051&partnerID=40&md5=6bf1eb7465b5582c930f7cd9c6d4d0cf},
doi = {10.3233/ATDE230051},
issn = {2352751X},
year = {2023},
date = {2023-01-01},
journal = {Advances in Transdisciplinary Engineering},
volume = {35},
pages = {248 – 257},
publisher = {IOS Press BV},
abstract = {Electricity is important in our modern life and also essential to the development of the country. Since the electricity load consumption in Thailand increases almost every year, the power systems capacity expansion is unavoidable. The purpose of this paper was to apply the machine learning algorithms to forecast the long-term electricity load consumption in Thailand. Three algorithms employed were artificial neural network (ANN), support vector regression (SVR), and k-nearest neighbors (KNN). The performances were evaluated in terms of mean absolute percentage error (MAPE). The results showed that the ANN outperformed other algorithms with a MAPE of 2.586%. © 2023 The authors and IOS Press.},
note = {Cited by: 0; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Electricity is important in our modern life and also essential to the development of the country. Since the electricity load consumption in Thailand increases almost every year, the power systems capacity expansion is unavoidable. The purpose of this paper was to apply the machine learning algorithms to forecast the long-term electricity load consumption in Thailand. Three algorithms employed were artificial neural network (ANN), support vector regression (SVR), and k-nearest neighbors (KNN). The performances were evaluated in terms of mean absolute percentage error (MAPE). The results showed that the ANN outperformed other algorithms with a MAPE of 2.586%. © 2023 The authors and IOS Press.
Nantachai Kantanantha, Pattarapol Pattaraumpornchai
Machine Learning-Based Price Forecasting for Polypropylene Granules in Thailand Conference
Association for Computing Machinery, 2023, ISBN: 979-840070906-7, (Cited by: 2; All Open Access, Hybrid Gold Open Access).
@conference{Kantanantha202314,
title = {Machine Learning-Based Price Forecasting for Polypropylene Granules in Thailand},
author = {Nantachai Kantanantha and Pattarapol Pattaraumpornchai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556375&doi=10.1145%2f3638209.3638212&partnerID=40&md5=2faf9af0e35be853961b28cc9e961ec1},
doi = {10.1145/3638209.3638212},
isbn = {979-840070906-7},
year = {2023},
date = {2023-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {14 – 19},
publisher = {Association for Computing Machinery},
abstract = {The plastic industry plays a vital role in Thailand, with a significant dependence on plastic materials for a majority of industrial products. Among the various types of plastics, polypropylene (PP) emerges as the most extensively used, making it indispensable for the country's plastic industry. This research focuses on presenting and comparing forecasting models for the price of PP granules in Thailand. The primary objective is to identify the most accurate forecasting model, with the mean absolute percentage error (MAPE) serving as the criterion for assessing the forecast model's performance. Three machine learning forecasting models, namely Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are employed in the study. The findings reveal that the ANN model demonstrates the highest accuracy, achieving a MAPE of 5.89% on the test dataset. © 2023 Owner/Author.},
note = {Cited by: 2; All Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The plastic industry plays a vital role in Thailand, with a significant dependence on plastic materials for a majority of industrial products. Among the various types of plastics, polypropylene (PP) emerges as the most extensively used, making it indispensable for the country’s plastic industry. This research focuses on presenting and comparing forecasting models for the price of PP granules in Thailand. The primary objective is to identify the most accurate forecasting model, with the mean absolute percentage error (MAPE) serving as the criterion for assessing the forecast model’s performance. Three machine learning forecasting models, namely Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are employed in the study. The findings reveal that the ANN model demonstrates the highest accuracy, achieving a MAPE of 5.89% on the test dataset. © 2023 Owner/Author.
2022
Nantachai Kantanantha, Jiaranai Awichanirost
Analyzing and forecasting online tour bookings using Google Analytics metrics Journal Article
In: Journal of Revenue and Pricing Management, vol. 21, no. 3, pp. 354 – 365, 2022, ISSN: 14766930, (Cited by: 3).
@article{Kantanantha2022354,
title = {Analyzing and forecasting online tour bookings using Google Analytics metrics},
author = {Nantachai Kantanantha and Jiaranai Awichanirost},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107937951&doi=10.1057%2fs41272-021-00338-7&partnerID=40&md5=df3d1df1f5396b824652d91b89e13eaf},
doi = {10.1057/s41272-021-00338-7},
issn = {14766930},
year = {2022},
date = {2022-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {21},
number = {3},
pages = {354 – 365},
publisher = {Palgrave Macmillan},
abstract = {An essential part of business operation for tourism industry is revenue management, i.e., how to sell the right tour package, to the right customers, at the right time, at the right price through the most appropriate and cost-effective channels. In today's world, the internet has revolutionized many business operations in the tourism industry which plays an important role in Thailand's GDP. Most tour operators utilize websites as the main channel to build relationships with customers. Thus, website performance measurement is an important strategic factor for online marketing. The objectives of this research were to identify factors contributing from Google Analytics metrics to online bookings and to forecast online bookings using those impactful factors. Several machine learning models namely artificial neural network (ANN), support vector regression, and random forest, were proposed to forecast online bookings using the mean absolute percentage error (MAPE) as the criterion for comparison. It was found that there were three Google Analytics metrics that contributed to online bookings, which were the sessions from referral, unique returning users, and the average session duration. In addition, the ANN model provided the highest accuracy result with a MAPE of 11.39%. The framework from this research can be applied to other online companies to forecast their online bookings, which is an important part of revenue management since accurate forecasts can help companies to achieve their goals. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An essential part of business operation for tourism industry is revenue management, i.e., how to sell the right tour package, to the right customers, at the right time, at the right price through the most appropriate and cost-effective channels. In today’s world, the internet has revolutionized many business operations in the tourism industry which plays an important role in Thailand’s GDP. Most tour operators utilize websites as the main channel to build relationships with customers. Thus, website performance measurement is an important strategic factor for online marketing. The objectives of this research were to identify factors contributing from Google Analytics metrics to online bookings and to forecast online bookings using those impactful factors. Several machine learning models namely artificial neural network (ANN), support vector regression, and random forest, were proposed to forecast online bookings using the mean absolute percentage error (MAPE) as the criterion for comparison. It was found that there were three Google Analytics metrics that contributed to online bookings, which were the sessions from referral, unique returning users, and the average session duration. In addition, the ANN model provided the highest accuracy result with a MAPE of 11.39%. The framework from this research can be applied to other online companies to forecast their online bookings, which is an important part of revenue management since accurate forecasts can help companies to achieve their goals. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
K. Chugh, N. Kantanantha
vol. 2022-December, IEEE Computer Society, 2022, ISSN: 21573611, (Cited by: 1).
@conference{Chugh2022390,
title = {Improving a Recommendation Engine for Traditional Trade Between Wholesalers and Retailers Using Association Rules},
author = {K. Chugh and N. Kantanantha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146310030&doi=10.1109%2fIEEM55944.2022.9989947&partnerID=40&md5=df706d02568ad54eaa0a9e296b5ba4bb},
doi = {10.1109/IEEM55944.2022.9989947},
issn = {21573611},
year = {2022},
date = {2022-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
volume = {2022-December},
pages = {390 – 394},
publisher = {IEEE Computer Society},
abstract = {This paper explores the data collection and mining of association rules to generate over 8,500 association rules which improve an existing recommendation engine used in an eCommerce platform between traditional trade wholesalers and traditional trade retailers. This improved recommendation engine allows traditional trade retailers to receive personalized recommendations based on items in their cart, and improves the current recommendation engine which only recommends most sold products. The improved recommendation engine helps traditional trade retailers purchase the right products for their stores and allows traditional trade wholesalers to increase the revenue of their stores, thereby providing both traditional trade wholesalers and traditional trade retailers with tools to help compete against modern trade outlets. © 2022 IEEE.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper explores the data collection and mining of association rules to generate over 8,500 association rules which improve an existing recommendation engine used in an eCommerce platform between traditional trade wholesalers and traditional trade retailers. This improved recommendation engine allows traditional trade retailers to receive personalized recommendations based on items in their cart, and improves the current recommendation engine which only recommends most sold products. The improved recommendation engine helps traditional trade retailers purchase the right products for their stores and allows traditional trade wholesalers to increase the revenue of their stores, thereby providing both traditional trade wholesalers and traditional trade retailers with tools to help compete against modern trade outlets. © 2022 IEEE.
2017
Panudet Saengseedam, Nantachai Kantanantha
Spatio-temporal model for crop yield forecasting Journal Article
In: Journal of Applied Statistics, vol. 44, no. 3, pp. 427 – 440, 2017, ISSN: 02664763, (Cited by: 7).
@article{Saengseedam2017427,
title = {Spatio-temporal model for crop yield forecasting},
author = {Panudet Saengseedam and Nantachai Kantanantha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964489816&doi=10.1080%2f02664763.2016.1174197&partnerID=40&md5=4df0bb9ecb3d645a6bd1511a61c4cb41},
doi = {10.1080/02664763.2016.1174197},
issn = {02664763},
year = {2017},
date = {2017-01-01},
journal = {Journal of Applied Statistics},
volume = {44},
number = {3},
pages = {427 – 440},
publisher = {Taylor and Francis Ltd.},
abstract = {This paper proposes a linear mixed model (LMM) with spatial effects, trend, seasonality and outliers for spatio-temporal time series data. A linear trend, dummy variables for seasonality, a binary method for outliers and a multivariate conditional autoregressive (MCAR) model for spatial effects are adopted. A Bayesian method using Gibbs sampling in Markov Chain Monte Carlo is used for parameter estimation. The proposed model is applied to forecast rice and cassava yields, a spatio-temporal data type, in Thailand. The data have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The proposed model is compared with our previous model, an LMM with MCAR, and a log transformed LMM with MCAR. We found that the proposed model is the most appropriate, using the mean absolute error criterion. It fits the data very well in both the fitting part and the validation part for both rice and cassava. Therefore, it is recommended to be a primary model for forecasting these types of spatio-temporal time series data. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
note = {Cited by: 7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a linear mixed model (LMM) with spatial effects, trend, seasonality and outliers for spatio-temporal time series data. A linear trend, dummy variables for seasonality, a binary method for outliers and a multivariate conditional autoregressive (MCAR) model for spatial effects are adopted. A Bayesian method using Gibbs sampling in Markov Chain Monte Carlo is used for parameter estimation. The proposed model is applied to forecast rice and cassava yields, a spatio-temporal data type, in Thailand. The data have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The proposed model is compared with our previous model, an LMM with MCAR, and a log transformed LMM with MCAR. We found that the proposed model is the most appropriate, using the mean absolute error criterion. It fits the data very well in both the fitting part and the validation part for both rice and cassava. Therefore, it is recommended to be a primary model for forecasting these types of spatio-temporal time series data. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Nantachai Kantanantha, Siriluk Runsewa
Forecasting of electricity demand to reduce the inventory cost of imported coal Conference
Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-150906774-9, (Cited by: 3).
@conference{Kantanantha2017336,
title = {Forecasting of electricity demand to reduce the inventory cost of imported coal},
author = {Nantachai Kantanantha and Siriluk Runsewa},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021448008&doi=10.1109%2fIEA.2017.7939233&partnerID=40&md5=77db5a1755b2ba3bbae5c906324d5a47},
doi = {10.1109/IEA.2017.7939233},
isbn = {978-150906774-9},
year = {2017},
date = {2017-01-01},
journal = {2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017},
pages = {336 – 340},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The purpose of this paper is to apply forecasting methods to forecast the electricity demand in Thailand. Demand forecasts will be used to estimate the imported coal order quantity in order to decrease the inventory cost of the imported coal. The monthly electricity demand data from January 2010 to December 2014 are used to forecast the monthly electricity demand from January 2015 to December 2015. The forecasting models are additive and multiplicative decomposition models and additive and multiplicative Holt-Winters models. The forecasting accuracies are measured by mean absolute percentage error and compared by randomized complete block design. The results of the study show that all forecasting accuracies are not significantly different so the multiplicative decomposition model is chosen because of its simplicity. The proposed imported coal order quantity is equal to 5.95 percent of the electricity demand forecasts. The inventory cost in 2015 decreased by 3,721.82 million baht or 14.84 percent compared to the inventory cost under the current order quantity. © 2017 IEEE.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The purpose of this paper is to apply forecasting methods to forecast the electricity demand in Thailand. Demand forecasts will be used to estimate the imported coal order quantity in order to decrease the inventory cost of the imported coal. The monthly electricity demand data from January 2010 to December 2014 are used to forecast the monthly electricity demand from January 2015 to December 2015. The forecasting models are additive and multiplicative decomposition models and additive and multiplicative Holt-Winters models. The forecasting accuracies are measured by mean absolute percentage error and compared by randomized complete block design. The results of the study show that all forecasting accuracies are not significantly different so the multiplicative decomposition model is chosen because of its simplicity. The proposed imported coal order quantity is equal to 5.95 percent of the electricity demand forecasts. The inventory cost in 2015 decreased by 3,721.82 million baht or 14.84 percent compared to the inventory cost under the current order quantity. © 2017 IEEE.
2016
Onuma Kosanan, Nantachai Kantanantha
vol. 8-10 March 2016, IEOM Society, 2016, ISSN: 21698767, (Cited by: 0).
@conference{Kosanan20161167,
title = {A hybrid particle swarm optimization algorithm and support vector machine model for agricultural statistic of Thailand forecasting},
author = {Onuma Kosanan and Nantachai Kantanantha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018390169&partnerID=40&md5=9ca9a5448201ce08da2270ea9b4187b6},
issn = {21698767},
year = {2016},
date = {2016-01-01},
journal = {Proceedings of the International Conference on Industrial Engineering and Operations Management},
volume = {8-10 March 2016},
pages = {1167 – 1178},
publisher = {IEOM Society},
abstract = {The objective of this research is to construct a Thailand's Para rubber production forecasting model. It will be advantageous to farmers, entrepreneurs and other organizations for the right planning and decision making in order to prepare themselves to be ready for the modernized global economics trends which will affect to Thailand's agricultural economy. Four forecasting techniques used in this research artificial neural network (ANN), particle swarm optimization algorithm (PSO), support vector machine (SVM) and hybrid model PSO and SVM. The mean absolute percentage error is used to identify the most appropriate model. The results of the research show that the hybrid PSO&SVM model obtains the lowest mean absolute percentage error of 0.0040%, while the particle swarm optimization model, support vector machine model and artificial neural network model have mean absolute percentage error of 0.0388%, 0.0388% and 0.0414% respectively. © IEOM Society International. © IEOM Society International.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to construct a Thailand’s Para rubber production forecasting model. It will be advantageous to farmers, entrepreneurs and other organizations for the right planning and decision making in order to prepare themselves to be ready for the modernized global economics trends which will affect to Thailand’s agricultural economy. Four forecasting techniques used in this research artificial neural network (ANN), particle swarm optimization algorithm (PSO), support vector machine (SVM) and hybrid model PSO and SVM. The mean absolute percentage error is used to identify the most appropriate model. The results of the research show that the hybrid PSO&SVM model obtains the lowest mean absolute percentage error of 0.0040%, while the particle swarm optimization model, support vector machine model and artificial neural network model have mean absolute percentage error of 0.0388%, 0.0388% and 0.0414% respectively. © IEOM Society International. © IEOM Society International.
2014
Onuma Kosanan, Nantachai Kantanantha
Thailand’s Para rubber production forecasting comparison Conference
vol. 2210, no. January, Newswood Limited, 2014, ISSN: 20780958, (Cited by: 3).
@conference{Kosanan2014,
title = {Thailand's Para rubber production forecasting comparison},
author = {Onuma Kosanan and Nantachai Kantanantha},
editor = {Castillo O. and Ao S.I. and Lee J.-A. and Douglas C. and Feng D.D.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938360201&partnerID=40&md5=162c634d135358a3e61e0b67febd8769},
issn = {20780958},
year = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Engineering and Computer Science},
volume = {2210},
number = {January},
publisher = {Newswood Limited},
abstract = {The objective of this research is to construct a Thailand's Para rubber production forecasting model. Three forecasting techniques used in this research are auto regressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM). The mean absolute percentage error is used to identify the most appropriate model. The results of the research show that the artificial neural network model obtains the lowest mean absolute percentage error of 0.0037%, while the auto regressive integrated moving average and support vector machine have mean absolute percentage error of 0.0419% and 0.0434%, respectively.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to construct a Thailand’s Para rubber production forecasting model. Three forecasting techniques used in this research are auto regressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM). The mean absolute percentage error is used to identify the most appropriate model. The results of the research show that the artificial neural network model obtains the lowest mean absolute percentage error of 0.0037%, while the auto regressive integrated moving average and support vector machine have mean absolute percentage error of 0.0419% and 0.0434%, respectively.
Panudet Saengseeedam, Nantachai Kantanantha
Spatial time series forecasts based on Bayesian linear mixed models for rice yields in Thailand Conference
vol. 2210, no. January, Newswood Limited, 2014, ISSN: 20780958, (Cited by: 0).
@conference{Saengseeedam2014,
title = {Spatial time series forecasts based on Bayesian linear mixed models for rice yields in Thailand},
author = {Panudet Saengseeedam and Nantachai Kantanantha},
editor = {Castillo O. and Ao S.I. and Lee J.-A. and Douglas C. and Feng D.D.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938335342&partnerID=40&md5=7c1e2b05d28cbba22d09dbd18777dbf0},
issn = {20780958},
year = {2014},
date = {2014-01-01},
journal = {Lecture Notes in Engineering and Computer Science},
volume = {2210},
number = {January},
publisher = {Newswood Limited},
abstract = {Spatial time series forecasts using linear mixed models (LMMs) with spatial effects under a Bayesian framework are considered. The random effects are assumed to be normally distributed and the spatial effects are assumed to be CAR models. The proposed model is applied to the rice yields data in 19 Northeastern provinces in Thailand. It has a better performance, using the MAE criteria, compared to the existing simple exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) models.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Spatial time series forecasts using linear mixed models (LMMs) with spatial effects under a Bayesian framework are considered. The random effects are assumed to be normally distributed and the spatial effects are assumed to be CAR models. The proposed model is applied to the rice yields data in 19 Northeastern provinces in Thailand. It has a better performance, using the MAE criteria, compared to the existing simple exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) models.