Forecasting from Big Data
A forecast is a forecast made by studying historical data and past patterns. Businesses use software tools and systems to analyse large volumes of data collected over time. Predictive research based on big data is usually divided into three main steps, namely data collection (collecting big data from relevant sources), data processing (pre-processing and representing big data and extracting predictive knowledge) and predictive improvement (by combining extracted predictive data). While there is a wide range of commonly used quantitative budget forecasting tools, in this article we focus on the first four methods: (1) straight line method, (2) moving average method, (3) simple linear regression method, and (4) multivariate Normal linear regression.
Related Conference of Forecasting from Big Data
6th Annual Summit on Satellite and Space Communication Technology
7th International Conference on Artificial Intelligence, Machine Learning and Robotics
10th World Congress on Computer Science, Machine Learning and Big Data
10th International Conference and Expo on Computer Graphics & Animation
Forecasting from Big Data Conference Speakers
Recommended Sessions
- Big Data Algorithm
- Big Data Analytics
- Big Data Applications, Challenges and Opportunities
- Big Data in Nursing Research
- Big Data Management
- Big Data Optimization
- Big Data Technologies
- Big Data Tools and Systems
- Cloud computing
- Data Mining and Machine Learning
- Data Mining Tasks, Processes and Analysis
- Data Mining Tools and Software
- Data Privacy and Ethics
- Data Warehousing
- Forecasting from Big Data
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