A Province Time-Series Data Prognostication Model-based Unconventional Research

Abstract

Numerous prediction issues include the extension of data or predictions because they have a temporal component. One of the most commonly used data mining techniques in business, the market, weather information, and pattern matching is series data forecasts. In order to look into the future, one must choose model that properly represent the available information. On the basis of the past, the future is projected or established. A time-order dependence is added to observations by time series. This dependence serves as a constraint as well as a foundation for more intelligence. This research presents an experimental examination of many cutting-edge time series prediction models. Data sets were analyzed, and the outcomes were assessed using the metrics MAE, MSE, RMSE, R2, and Estimated number per datapoint.