Forecasting of agrarian commodity prices by time series methods

It is very important for farm management to correctly estimate future demand and agricultural commodity price developments. In the paper, we will use the decomposition of time series into a linear trend component and seasonal component using Fisher's periodogram. Our numerical studies work with time series of selected agrarian commodities on the Commodity Exchange. The theoretical background, assumptions, advantages and disadvantages of methods are also briefly presented and discussed. In the discussion section, the success of the implemented algorithm is measured. We will work with the prices of the period from January 1970 to December 2020 for agrarian commodities from the New York Board of Trade (NYBOT). We will focus on the prediction of the price development of wheat and oat.