Discover how Fourier Analysis breaks down complex time series data into simpler components to identify trends and patterns, despite its limitations in stock forecasting.

The Physician Workforce Supply and Demand Estimation Committee, which is discussing the size of medical school enrollment, ...

20 Superstars, two matches, one word... WarGames! The annual Survivor Series Premium Live Event returns on Saturday, November 29, when WWE takes over Petco Park and transforms the home of Major League ...

A comprehensive comparison of time series forecasting techniques applied to hourly energy consumption data, from classical statistical models (ARIMA, SARIMAX) to modern approaches (Prophet, N-HiTS).

Abstract: The purpose of this paper is to predict and analyze the number of medals of Olympic countries based on ARIMA time series model and random forest. Firstly, the ARIMA time series model and ...

Abstract: Numerous forecasting issues involve time, that's why time series forecasting is an essential component of machine learning. Since computer resources have gotten better, it is now possible to ...

This paper presents a comparative study of ARIMA and Neural Network AutoRegressive (NNAR) models for time series forecasting. The study focuses on simulated data generated using ARIMA(1, 1, 0) and ...

Total nitrogen was an important indicator for characterizing eutrophication of polluted water. Although the use of water quality online monitoring instrument can monitor water quality changes in real ...

Faculty of Economic and Management Sciences, Department of Business Statistics and Operations Research, North-West University, Mmabatho, South Africa Given the numerous factors that can influence ...

Deep learning models have made great accomplishments in space weather forecasting. The critical frequency of the ionospheric F2 layer (foF2) is a key ionospheric parameter, which can be understood and ...