การประยุกต์ใช้การวิเคราะห์อนุกรมเวลาในการพยากรณ์ราคาสุกรพันธุ์ลูกผสมของประเทศไทย (APPLICATION OF TIME SERIES ANALYSIS TO FORECAST CROSSBRED SWINE PRICE IN THAILAND)

วัตถุประสงค์ในการศึกษาครั้งนี้ คือ สร้างตัวแบบพยากรณ์ที่เหมาะสมสำหรับข้อมูลราคาสุกรพันธุ์ลูกผสมรายเดือนของประเทศไทย เพื่อใช้เป็นข้อมูลพื้นฐานในการวางแผนการผลิตให้สอดคล้องกับความผันผวนของราคาสุกร โดยใช้ข้อมูลจากสำนักงานเศรษฐกิจการเกษตร ตั้งแต่เดือนมกราคม พ.ศ. 2551 ถึง เดือนธันวาคม พ.ศ. 2562 มาสร้างตัวแบบพยากรณ์ทั้งหมด 3 วิธี ได้แก่ วิธีการปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวก วิธีบอกซ์-เจนกินส์ และการพยากรณ์รวม เพื่อเลือกวิธีที่เหมาะสมที่สุด จากเกณฑ์ค่าเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์เฉลี่ยและค่ารากที่สองของความคลาดเคลื่อนกำลังสองเฉลี่ยในการวัดความคลาดเคลื่อนของการพยากรณ์ ผลการศึกษาพบว่า วิธีที่แม่นยำที่สุด คือ การพยากรณ์รวม โดยค่าพยากรณ์เดือนมกราคม ถึง เดือนธันวาคม ปี พ.ศ. 2563-2564 จากวิธีนี้ได้ว่า ราคาสุกรพันธุ์ลูกผสมในช่วงเดือนพฤษภาคม ถึง เดือนสิงหาคมมีราคาสูง อยู่ในช่วง 67.06-68.47 บาทต่อกิโลกรัม และช่วงเดือนธันวาคม ถึง มกราคมมีราคาต่ำ ซึ่งอยู่ในช่วง 60.47-60.80 บาทต่อกิโลกรัม จากผลการพยากรณ์เกษตรกรและผู้ที่เกี่ยวข้องสามารถนำไปใช้ในการวางแผนการผลิตสุกรให้สอดคล้องกับช่วงที่ราคาต่ำและสูงคำสำคัญ: ราคาสุกรพันธุ์ลูกผสม  การพยากรณ์  วิธีวินเทอร์  การพยากรณ์รวมThe aim of the study was to develop forecasting model for crossbred swine price in Thailand. The results can be database in production planning to correspond with variation of swine price. The data was collected from Office of Agricultural Economics over consecutive months from the period January 2008 – December 2019. There are three forecasting models considering to be fitted with the data such as Winter’s additive exponential smoothing method, Box-Jenkins method and combined forecasting method. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) were used to compare accuracy of model. The result showed that the most appropriated model was combined forecasting method. The forecasting values in January to December 2020-2021 from this model showed that the highest crossbred swine price would be in May to August period (67.06-68.47 baht per kilogram) and the lowest crossbred swine price would be in December to January period (60.47-60.80 baht per kilogram). Farmers can apply these forecasts for planning in swine production in accordance to low and high price period.Keywords: Crossbred Swine Price, Forecasting, Winter’s Method, Combined Forecastin

Photovoltaic Forecasting: A state of the art

International audiencePhotovoltaic (PV) energy, together with other renewable energy sources, has been undergoing a rapid development in recent years. Integration of intermittent energy sources as PV or wind power is challenging in terms of power system management in large scale systems as well as in small grids. Indeed, PV energy is a variable resource that is difficult to predict due to meteorological uncertainty. To facilitate the penetration of PV energy, forecasting methods and techniques have been used. Being able to predict the future behavior of a PV plant is very important in order to schedule and manage the alternative supplies and the reserves. In this paper we presented an overview aiming at a classification attending to the different techniques of forecasting methods used for PV or solar prediction. Finally, recent new approaches that take into account the uncertainty of the estimation are introduced. First results of these kind of models are presented

Demand Prediction Using Machine Learning Methods and Stacked Generalization

Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.Comment: Proceedings of the 6th International Conference on Data Science, Technology and Application

Use of Artificial Intelligence (AI) in Managing Inventory of Medicine in Pharmaceutical Industry

 Inventory is one of the vital components of current assets. Excess holdings of inventory may increase cost as well as wastage. As such, effective and efficient management of inventory is an integral part of supply chain. Especially, in the field of management of pharmaceutical products and medicine it bears more importance. Improper use of pharmaceutical products or shortage of medicine would not only cause financial loss but also may affect the patients adversely. Rather than using the traditional techniques of managing inventory use of Artificial Intelligence (AI) can make the process more effective and efficient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study has been conducted based mainly on secondary sources of data. It is a qualitative study that gives a conceptual idea regarding how the functions of AI can support managing inventory of medicine in pharmaceutical industry

Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers

Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand

A Suitable Artificial Intelligence Model for Inventory Level Optimization

Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise