A trend prediction model from very short term data learning

Abstract

Currently, the environment has dynamic and changeable characteristics, making previously collected data unsuitable for building a predictive model, in that the value of sample population parameters such as mean or variance is moving or fluctuating. However, up-to-date data is usually in small sample sets, and it is risky to assume that the derived distribution; such as the normal distribution, from a few collected samples is an unbiased estimation of the underlying population. Based on this fact, the sample statistic X ¯ may simply not be the proper measurement to estimate the mean of a population when confronting small data sets. This research proposes the Central Location Tracking Method (CLTM), with the novel concept of a “trend center”, that is the center of probability ( CP) determined by a variety of derived data properties which is employed to estimate the probable location of the population center μ . This approach aims at obtaining better predictability and fewer estimation errors for small sample sets. The comparison results between the method presented and X ¯ , regression, neural networks, and ARIMA methods validate the superiority of this method for both random data and dependent data.