Abstract
As an initial research in human mobility, human mobility prediction can be done by using a time-series predictor algorithm, one of which is ARIMA. ARIMA is short of the integrated moving-average autoregressive. The order of the ARIMA model is represented by the ARIMA symbol (p, d, q), where p is the order of the autoregressive part, d is the order of differencing and q is the order of moving-average process. The research regarding the application of human mobility conducted through five phases, including data collection, data pre-processing, data model building, data prediction and data evaluation. We conducted three times of experiments with different parameters. We defined different value for D, Seasonality, MALags, SMALags and Variance. Based on experiment as conclusion of this research obtained that the best parameter values to get the best MAPE Longitude and MAPE Latitude are Constant = 0, D= 1, Seasonality= 12, MALags = 1, SMALags=12 with MAPE Lon: 0.037433% and MAPE Lat: 0.11632%
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
