Time-series analysis and statistical forecasting of daily rainfall in Kupang, East Nusa Tenggara, Indonesia: a pilot study

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


There are no comments or no comments have been made public for this article.


Download Preprint

Supplementary Files

Sandy Hardian Susanto Herho , Gisma Aminurah Firdaus


This pilot study presents a novel statistical time-series approach for analyzing daily rainfall data in Kupang, East Nusa Tenggara, Indonesia. By using the piecewise cubic hermite interpolation algorithm, we succeeded in filling in the null values in the daily rainfall time series. We then analyzed the monthly average and its pattern using the continuous wavelet transform (CWT) algorithm, which shows the strong annual pattern of rainfall in this region. In addition, we use the rainfall anomaly index (RAI) function to standardize daily rainfall as an indicator of dry/wet conditions in this region. Then we also use the daily RAI time-series objects from 1978 to 2020 for modeling and predicting daily RAI over the next year. The result is the root mean squared error (RMSE) of 0.8424041040593219. This Prophet model is also able to capture the linear trend of increasing drought throughout the study time period and the annual pattern of wet/dry conditions which is in accordance with previous study by Aldrian and Susanto (2003).




Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability


rainfall anomaly, facebook prophet, Wavelet transform, Time-series analysis


Published: 2022-01-06 01:01

Last Updated: 2022-01-06 01:01


CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:

Data Availability (Reason not available):