Big Data in Forecasting Research: A Literature Review

Abstract

With the boom in Internet techniques and computer science, a variety of big data have been introduced into forecasting research, bringing new knowledge and improving prediction models. This paper is the first attempt to conduct a literature review on full-scale big data in forecasting research. By source, big data in forecasting research fell into user-generated content data (from the users on social media in texts, photos, etc.), device-monitored data (by meteorological monitors, smart meters, GPS, etc.) and activity log data (for web searching/visiting, online/offline marketing, clinical treatments, laboratory experiments, etc.). Different data types, bearing distinctive information and characteristics, dominated different forecasting tasks, required different analysis technologies and improved different forecasting models. This survey provides an overall review of big data-based forecasting research, details what (regarding data types and sources), where (forecasting hotspots) and how (analysis and forecasting methods used) big data improved prediction, and offers insights into future prospects. • A review on full-scale big data in forecasting research is presented. • Big data in forecasting research fell into UGC data, device data and log data. • Three steps were taken: data collection, data process and prediction improvement. • For each data type, what, where and how big data improved prediction are detailed.