Extrapolation-based grey model for small-data-set forecasting

Product life cycles have become increasingly shorter owing to the rise of global competition in recent decades. Competitive tension is especially high in electronics-related industries. It is usually difficult for most enterprises to collect sufficient quantities of samples with which to obtain useful information when making decisions in such a highly competitive environment. Grey system theory plays a vital role in addressing the issue of insufficient sample quantities. The traditional GM(1,1) model is well known for its ability to generate useful forecasts with a small quantity of samples; however, the newest datum is always weakened to alleviate the randomness of data in the traditional GM(1,1) model, causing it to output higher prediction errors. To overcome such imperfections, this study proposes a modified grey forecasting model named EP-GM(1,1), in which a new equation for calculating the background values in the traditional GM(1,1) model is developed based on linear extrapolation to emphasize the importance of the newest datum. To evaluate the forecasting ability of EP-GM(1,1), the monthly demand of thin-film-transistor liquid-crystal display panels were employed for experimentation. The results indicate that EP-GM(1,1) can engender a favorable prediction result, demonstrating that the model is a feasible tool for small-sample forecasting.