การศึกษาการเรียนรู้ของเครื่องสำหรับการทำนายราคาน้ำมันปาล์มดิบ
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วามนี้มุ่งเน้นไปที่การคาดการณ์ราคาน้ำมันปาล์มดิบในประเทศไทยเพื่อหาโมเดลที่ดีที่สุดในการทำนายราคา การคาดการณ์ราคาที่แม่นยำซึ่งเป็นสิ่งสำคัญสำหรับผู้มีส่วนได้ส่วนเสียในอุตสาหกรรมน้ำมันปาล์ม เพื่อช่วยในการตัดสินใจและสามารถจัดการกับความเสี่ยงอย่างมีประสิทธิภาพ ในการศึกษานี้เป็นการเปรียบเทียบแบบจำลอง 3 แบบ ได้แก่ โมเดลพื้นฐาน (Baseline Model) โมเดล RNN-LSTM และโมเดลคลาสสิก (Classic Model) เพื่อทำนายราคาน้ำมันปาล์มดิบในประเทศไทย โดยโมเดลคลาสสิก ได้แก่ Regression Model, Exponential Smoothing, Random Forest, LightGBM Model and Theta Method ดำเนินการทดสอบเปรียบเทียบประสิทธิภาพของโมเดลต่าง ๆ ในการทำนายราคาของน้ำมันปาล์มดิบโดยใช้อนุกรมเวลาเป็นปัจจัยเดียวในการทดสอบ ในการประเมินประสิทธิภาพใช้ Mean Absolute Percentage Error (MAPE) เพื่อวัดเปอร์เซ็นต์ความเบี่ยงเบนระหว่างราคาที่คาดการณ์และราคาที่เป็นจริง โดยเป็นการให้ข้อมูลเชิงลึกเกี่ยวกับประสิทธิภาพของแบบจำลอง ผลการศึกษาแสดงให้เห็นว่าโมเดลพื้นฐานมีค่า MAPE ต่ำที่สุดอยู่ที่ 0.08128 ดังนั้นแบบจำลองที่พื้นฐานจึงมีประสิทธิภาพดีที่สุดในการพยากรณ์ราคาน้ำมันปาล์มดิบในประเทศไทย
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