Predicting Future Event via Small Data (e.g., 4 Data) by ASF and Curve Fitting Methods

Abstract

Future prediction is a branch of information processing. An attempt to predict some future event year is presented in this work via combining the addition-subtraction frequency (ASF) method, i.e., ASF algorithms with 3 inputs, and multiple mathematical modeling methods (e.g., polynomial curve fitting, exponential curve fitting, and smoothing spline). The 3-input ASF algorithms using full-traversal, equal-half-traversal, and unequal-half-traversal are applied in the numerical experiments. The difficult challenge we face is that the raw data set size is small, i.e., only 4. Thus, we process the limited information in a variety of ways, i.e., we handle the small data set by using multiple methods. We finally predict that 2021, 2022, or 2027 is of relatively high possibility to be a future year of such a small-data sequence. There may be errors of one to two years, and it may be avoided if some proper measures are taken.