KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Search Results

Now showing 1 - 8 of 8

Some of the metrics are blocked by your
consent settings
Forecasting Models for Thailand's Electrical Appliances Export Values
(2023-01-01)
Banditvilai, Somsri
;
Klomwises, Yuwadee
This research aimed to study forecasting models for Thailand's electrical appliances export values. Thailand's monthly electrical appliances export values were gathered from the Information Technology and Communication Center, Ministry of Commerce, from January 2006 to November 2022. The data from January 2006 to December 2021 were used to construct and select the forecasting models, and the remaining were used for measuring the model's accuracy. Since the electrical appliances export values showed trends and seasonal variation, the researcher selected the Holt-Winters method with various initial settings, the Box-Jenkins method, and Long Short-Term Memory Neural Networks (LSTM) for constructing models. The forecasting models were chosen by minimum Root Mean Square Error (RMSE) as a criterion. Mean Absolute Percentage Error (MAPE) was employed to measure the accuracy of the forecasting model. The study revealed that the Box-Jenkins model gave the appropriate forecasting model for Thailand's electrical appliances export values and gained a MAPE of 8.0%.
Some of the metrics are blocked by your
consent settings
FORECASTING MODELS FOR FIRST YEAR PREMIUM OF LIFE INSURANCE
(2022-01-01)
Banditvilai, Somsri
;
Kuharattanachai, Choojai
The objective of this research is to study forecasting models for the first year premium of life insurance. The premium data are gathered from the Office of Insurance Commission (OIC) during January 2003 to November, 2021. The data are divided into 2 sets. The first set from January, 2003 to December 2020 is used for constructing and selection the forecasting models. The second one from January 2021 to November 2021 is used for computing the accuracy of the forecasting model. The forecasting models are chosen by considering the minimum Root Mean Square Error (RMSE). The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the model. The results showed that the multiplicative model with initial values from 18 years Decomposition method give the appropriate model for the first year premium of life insurance and yields the MAPE = 17.29%
Some of the metrics are blocked by your
consent settings
Air turbulence forecasting of airbus type A320 in southeast asia using deep learning method
(2020-11-04)
Laon, Popphon
;
Phasukkit, Pattarapong
;
Pradabpet, Chusit
This paper is present the Air Turbulence Forecasting of Airbus type A320 in Southeast Asia using the Deep Learning Method. It will collect flight data of aircraft in the region of Southeast Asia (Thailand and Vietnam). In which data was collected for 40 flights to forecast the occurrence of air turbulence in 3 status consist of non-turbulence, the rapid decrease and increase the altitude is out of pilot control. In this research using deep learning with supervise learning and create the mathematical model for air turbulence forecasting which could help to reduce the wastage that may affect to the passenger. The results, using 5 layers of deep learning (1 input layer, 3 hidden layers, and 1 output layer). The most suitable model consists of 9 features such as vertical speed, calibrated altitude, wind speed, wind direction (wind angle), temperature, latitude, longitude, true airspeed, and indicated airspeed. The output layer consists of 3 classes (class1=non-turbulence, class2=increase altitude, and class3=decrease altitude) and optimization the weight with gradient descent. The epoch number is 1500 and the learning rate is 0.1, which will get accuracy 88% for the train set and 86 % for the test set.
Some of the metrics are blocked by your
consent settings
An application of convolutional neural network-long short-term memory model for service demand forecasting
(2019-07-01)
Phankokkruad, Manop
;
Wacharawichanant, Sirirat
The medical services are very important requirement for being healthy human. In order to ensure the availability of resources for the medicine needed, the most hospital makes an service demand estimation by forecasting a number of patients to provide the sufficient medical services. Therefore, the accurately forecast a number of patients would be valuable knowledge for managing. This work proposed the CNN-LSTM model, which was a combination of CNN and LSTM, to forecast the number of patients who used hospital services. The CNN model was used to interpret, and extract the features from the input data. Then, it was provided this information to the LSTM model for interpreting and making a forecast. The CNN-LSTM models were applied to forecast on the two datasets. The results indicated that CNN-LSTM model made reliable forecasting. This work measured the model performnace by calculating RMSE and MAE value. The result showed RMSE and MAE of the models were very low in all experiments. Forecasting the number of patients can help the hospital to estimate the service demand, make a better policy for managing the medical resources on demand, and improve the efficiency of medical services for the future.
Some of the metrics are blocked by your
consent settings
Forecasting export value in the automobile industry
(2018-06-20)
Chaiwuttisak, Pornpimol
The study aims to investigate the appropriate time series techniques for forecasting export values of cars and auto parts (Million Baht). The monthly data are gathered from the website of Ministry of Commerce from January 2010 to December 2017. The data are divided to two datasets. One dataset during January 2010 and December 2016 is consists of 84 observations which is used for build the forecasting model by using Winters' Additive Exponential Smoothing and Box- Jenkins. Another dataset between January 2017 and December 2017 are used for comparing forecast accuracy based on the lowest value of Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) and selecting the most appropriate forecasting model. The result shows that Box- Jenkins is the best method for this time series data.
Some of the metrics are blocked by your
consent settings
Modeling haze problems in the North of Thailand using logistic regression
(2014-01-01)
Pimpunchat, Busayamas
;
Sirimangkhala, Khwansiri
;
Junyapoon, Suwannee
At present, air pollution is a major problem in the upper northern region of Thailand. Air pollutants have an effect on human health, the economy and the traveling industry. The severity of this problem clearly appears every year during the dry season, from February to April. In particular it becomes very serious in March, especially in Chiang Mai province where smoke haze is a major issue. This study looked into related data from 2005-2010 covering eight principal parameters: PM10 (particulate matter with a diameter smaller than 10 micrometer), CO (carbon monoxide), NO<inf>2</inf> (nitrogen dioxide), SO<inf>2</inf> (sulphur dioxide), RH (relative humidity), NO (nitrogen oxide), pressure, and rainfall. Overall haze problem occurrence was calculated from a logistic regression model. Its dependence on the eight parameters stated above was determined for design conditions using the correlation coefficients with PM10. The proposed overall haze problem modeling can be used as a quantitative assessment criterion for supporting decision making to protect human health. This study proposed to predict haze problem occurrence in 2011. The agreement of the results from the mathematical model with actual measured PM10 concentration data from the Pollution Control Department was quite satisfactory.
Some of the metrics are blocked by your
consent settings
Forecasting Thailand's rice export: Statistical techniques vs. artificial neural networks
(2007-11-01)
Co, Henry C.
;
Boosarawongse, Rujirek
Forecasting the international trade of rice is difficult because demand and supply are affected by many unpredictable factors (e.g., trade barriers and subsidies, agricultural and environmental factors, meteorological factors, biophysical factors, changing demographics, etc.) that interact in a complex manner. This paper compares the performance of artificial neural networks (ANNs) with exponential smoothing and ARIMA models in forecasting rice exports from Thailand. To ascertain that the models can reproduce acceptable results on unseen future, we evaluated various aggregate measures of forecast error (MAE, MSE, MAPE, and RMSE) during the validation process of the models. The results reveal that while the Holt-Winters and the Box-Jenkins models showed satisfactory goodness of fit, the models did not perform as well in predicting unseen data during validation. On the other hand, the ANNs performed relatively well as they were able to track the dynamic non-linear trend and seasonality, and the interactions between them. © 2007 Elsevier Ltd. All rights reserved.
Some of the metrics are blocked by your
consent settings
A new fuzzy-neural system for time series forecasting
(2005-11-30)
Thammano, Arit
;
Palahan, Sirinda
This paper proposes a new time series forecasting system, whose learning algorithm is a hybrid of the fuzzy c-means algorithm, the genetic algorithm, and the backpropagation algorithm. The proposed fuzzy-neural system consists of 5 layers: the input layer, the fuzzification layer, the rule layer, the hidden layer, and the output layer. The fuzzy cmeans algorithm is used to determine the center and width of the fuzzy membership functions. The artificial neural network is used as the fuzzy inference engine, while the genetic algorithm is used to optimize the fuzzy rule-base. This proposed system is tested with five time series data. The results obtained are very encouraging.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results