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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Precipitation Missing Data Prediction Using Recommendation System

[Full Text]

 

AUTHOR(S)

Herdianti Darwis, Fitiyani Umar

 

KEYWORDS

Missing data, prediction; forecasting; recommendation system; matrix decomposition; RMSE; MAE.

 

ABSTRACT

Complete data is generally required in data analysis especially in time-series-related study. However, incomplete data due to equipment malfunction, human error, disaster, or other unknown reason is practically discovered. It is required to perform missing data prediction before forecasting the future values. Recommendation system is a system that predicts the "rating" or "preference" of a user over an item. Instead of dealing to a function of time series, the weekly precipitation data of Makassar City is placed into a matrix form consisting of "years" in row as the users and "weeks of the year" in column as the items. This method is also known as matrix decomposition. Accuracy of prediction by root mean square error (RMSE) and mean absolute error (MAE) have been performed to compare the predicted result by using the matrix decomposition to the observed values. In this study, matrix decomposition is discovered as a reliable method in dealing with the missing values of historical observation and forecasting the future values simultaneously.

 

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