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 over(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 over(X, -), regression, neural networks, and ARIMA methods validate the superiority of this method for both random data and dependent data.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence