Expectation-Based Probabilistic Naive Approach for Forecasting Involving Optimized Parameter Estimation

This paper presents a forecasting technique based on the principle of naïve approach imposed in a probabilistic sense, thus allowing to express the prediction as the statistical expectation of known observations with a weight involving an unknown parameter. This parameter is learnt from the given data through minimization of error. The theoretical foundation is laid out, and the resulting algorithm is concisely summarized. Finally, the technique is validated on several test functions (and compared with ARIMA and Holt–Winters), special sequences and real-life covid-19 data. Favorable results are obtained in every case, and important insight about the functioning of the technique is gained.