Predicting chiller system performance using ARIMA-regression models

Predicting Performance on MOOC Assessments using Multi-Regression Models

The past few years has seen the rapid growth of data mining approaches for the analysis of data obtained from Massive Open Online Courses (MOOCs). The objectives of this study are to develop approaches to predict the scores a student may achieve on a given grade-related assessment based on information, considered as prior performance or prior activity in the course. We develop a personalized li...

Small-sample Forecasting Regression or Arima Models?

Investigating the accuracy of multivariate regression and ARIMA models in predicting water demand (Case Study: Mashhad city)

Awareness of water demand is of particular importance for its policy in urban management. Predicting water demand in the future will allow managers to take the necessary measures regarding sustainable water supply, given the constraints and crises ahead. The purpose of this study is to compare multivariate regression and ARIMA models to predict water demand in Mashhad. In this study, first, the...

Improving the performance of financial forecasting using different combination architectures of ARIMA and ANN models

Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy. One of the most well established and widely use...

Forecasting irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...