Rare earth elements price forecasting by means of transgenic time series developed with ARIMA models

A time series can be thought of as a numerical organism with a continuous nature from a chronological point of view and something that is permanently updated. Up to this moment time series research related with their features, traits, and characteristics, is mainly focused on data mining, in order to discover hidden information or specific knowledge within the time series or their transformations. However, time series representation is crucial, as they are difficult to handle in their original structure due to their high dimensionality. In this paper, the theory of transgenic time series is developed, and applied to the forecasting of several rare earth oxide prices: dysprosium, europium, terbium, neodymium, and praseodymium oxides. This theory addresses, specifically, the existence of metal price cycles and the presence of anomalous phenomena that the theory allows to eliminate from the time series, improving the accuracy of the forecast. After representing the time series in a way that allows their genome to be sequenced, a restriction enzyme is defined in order to create a genetically modified time series. There was no need to develop DNA ligases as time series can be cut and pasted without further considerations. Results clearly state that transgenic time series lead to more accurate short term forecasts in cases where a consistent time series genome can be represented. Further research should address the feasibility of developing more accurate long term forecasts by adding new gene sequences based on the time series genome, in order to achieve greater confidence from investors and professional advisers in the feasibility studies developed for future mining investment projects. Finally, it has to be remarked that this theory has nothing to do with genetic algorithms, a metaheuristic that was inspired by the natural selection process and not by the sequencing and manipulation of the genome.