Dynamic Data Sampling Approach by Using Price Distribution of Crude Palm Oil to Forecast High Magnitude Price Movement

Traditionally the chemical industry uses coal, minerals and petroleum as its basic raw materials, but palm oil and palm kernel oil are being increasingly used as economical raw materials especially for the production of oleochemicals. High magnitude palm oil price volatility in recently years has been a major challenge faced the industry. Though many time series models have been developed, few have wide adoption in the industry, and one of the key issues is the sampling interval used in the models. To date, little effort has been spent on mining historical data to determine the representativeness of interval sampling. This paper presents a novel approach in identifying price equilibrium for crude palm oil by mining the sampling amount through historical price distribution. Evaluation is done on the outcomes of the experiment, and analysis is performed on the attributes of each different criteria of the price distribution. The performance of the proposed approach is also compared to the conventional Bollinger Band with static sampling size. Overall, the preliminary results show that price distribution with leptokurtic distribution outperforms other price distribution patterns, this will definitely assist further works to devise a novel financial time series analysis technique.