Akanit Kwangkaew. Impact of power consumption on stock price prediction in manufacturing sector by ANN approach. Master's Degree(Information and Communication Technology for Embedded Systems). Thammasat University. Thammasat University Library. : Thammasat University, 2017.
Impact of power consumption on stock price prediction in manufacturing sector by ANN approach
Abstract:
Economy growth system in each manufacturing country mostly depends on the manufacturing sector which uses electric energy to produce their products. Electric energy consumption (EPC), which can be easily converted into other energy types, plays an important role in producing the products in the manufacturing sectors. In this research, stock price (SP), reflexing the economic trends, are predicted by adding an EPC input feature in particular manufacturing sector. It improves the performance of the prediction to prove the relationship between EPC and SP. To tackle the restriction of quantity of type of data sets, Cross Domain Checking (XDC) method is proposed for the prediction to eliminate the issue. According to an experiment, it was found that two data sets, automobile part and food processing factory, which is the different domain, can be crossed domain to predict SP by techniques in this research. This is a concept of XDC which a prediction uses data to be a training set from the different factories in manufacturing sector. This is useful for a lacking data set problem. The XDC method was also used to approximately predict the trend of SPs in disaster period(Flooding in 2011 of Thailand). It is helpful to predict the trend of SPs even in an unexpected situation by using small data set. According to observations, in the disaster period, EPC dramatically decreased at the beginning. After that, the trend of SPs fluctuates and takes small rest period which is called time-delay that can be measured by Monthly Time-Delayed Consequence (MTDC) Detection proposed method.
Thammasat University. Thammasat University Library
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