PALMOILMAGAZINE, SINGAPORE – Once the influencing factors behind crude palm oil (CPO) prices are understood, the subsequent step involves delving into prediction techniques to gain deeper insights.
Advanced CPO price prediction techniques focus on analyzing historical data to identify trends and patterns that can aid in future price forecasting. This approach considers factors such as supply and demand, weather conditions, government policies, and the global economic situation.
Moreover, employing artificial intelligence algorithms enhances data analysis efficiency by handling complex and diverse datasets. This integration of AI technology enables more precise and accurate predictions.
As Palmoilmagazine.com quoted from Palm Oil Anlytic, the next thing is to get fundamental analysis, which is by noticing the fundamental factors that have something to do with the markets, such as, production, and demands. “The analysis would deliver valuable outlook about its price progress,” Palm Oil Anlytic noted.
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Understanding Global Factors Influencing CPO Price Predictions
By combining many approaches with deeper understanding about the factors influencing CPO price, it would deliver competitive mainstay to get business and investment decision.
Meanwhile, in history, market and researchers’ analysis used many traditional methods to predict CPO price. The methods rely on statistic and historical data analysis model to predict it. “Though traditional methods could deliver some outlooks, these methods often failed to get dynamic complexity about CPO price,” Palm Oil Analytic noted, Monday (15/4/2024).
Moving Average is the simple method but it is mostly used to predict CPO price. This method calculates average price during a certain period and is used to predict the future price. Moving average would smooth short term fluctuation price and deliver trend line to predict its price.
Linear Regression is the other common technic to predict CPO price. It involves liner regession to the historic data and uses the ine to predict the future price. Linear regression assumed that there is linear connection between free variable (in this case period of time) and dependent variable (price).
ARIMA model. ARIMA stands for autoregressive integrated moving average. It is popular to predict time row, including CPO price. This model considers autoregressive (AR) components that capture the connection between the past price, and moving average (MA) that also captures the error of the rests.
Though traditional method delivers the basic to predict CPO price, this method has its limits to capture the complexity and non-linear characteristics from CPO price dynamics. The advanced CPO price prediction level, such as, machine learning algorithm would offer more accurate and stronger approach. (T2)
