Malaysia is very well-known as the world leader of palm oil in terms of production and export. As of today, palm oil has contributed the most in the Malaysian agricultural sector. This illustrates the importance of the palm oil industry in Malaysia. In situations of considerable uncertainty and therefore high risk, accurate and reliable price forecasting is necessary to facilitate decision making. In doing so, this study developed a CPO price forecasting method using Fuzzy Time Series with the proposed of Sliding Window Method. Besides, the concept of Fuzzy Rule Based Systems (FRBS) also was embedded in the application of Fuzzy Time Series. The aim of the proposed method is to enhance the effectiveness of time series forecasting and to provide higher predicting accuracy. The dataset of Crude Palm Oil (CPO) prices were taken from Malaysian Palm Oil Board (MPOB) and used to compare forecasting performance between several method such as Autoregressive Moving Average, ARIMA (Box-Jenkins model), Artificial Neural Network (ANN) and Hybrid ARIMA-ANN methods. The accuracy of all methods was compared to each other to determine the best forecasting method. In this study, the results showed that the forecast value of CPO price using all the method produces good and reliable CPO prices forecasting values. In spite of that, the forecast error of the proposed method and hybrid ARIMA-ANN method is lesser compared to other methods. This can be summarized that both these methods can reduce and perform better forecasting values, yet the hybrid ARIMA-ANN was known with its complexity of the method. Meanwhile, Fuzzy Time Series forecasting with the proposed method provides more proper and simpler method to forecast CPO price. Hence, the findings of this study could be used as an alternative method for CPO price forecasting to obtain a better forecast values. © Published under licence by IOP Publishing Ltd.