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Usmani, U.A., Abdul Aziz, I., Jaafar, J. and Watada, J. (2024) Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research. IEEE Access, 12, 174564-174590.
https://doi.org/10.1109/access.2024.3495819

has been cited by the following article:

• TITLE: Prediction of Time Series Rain-Induced Attenuation Using SARIMA and LSTM Models over Subtropical Climate: A Comparison

  AUTHORS: Adewumi Oluwatoyin Ayo, Pius Adewale Owolawi, Joseph Sunday Ojo

  KEYWORDS: Rain Attenuation, SARIMA Model, LSTM, Signal Attenuation, Auto Correlation Function (ACF), Time Series Analysis, Akaike Information Criterion (AIC), Artificial Neural Networks (ANN), Partial Autocorrelation Function (PACF)

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.4, April 29, 2026

  ABSTRACT: Rain fading affects terrestrial and satellite communication networks operating in higher frequencies (Ku and above), leading to signal absorption and dispersion. Rain-induced attenuation prediction is therefore vital to achieving good signal quality at both terrestrial and satellite links. The magnitude and extent of signal attenuation or loss are the primary factors to consider when planning and designing terrestrial and satellite communication links. The proposed method can anticipate datasets, whether linear or non-linear, for a temporal series of yearly attenuation patterns, modelling, and prediction in subtropical locations. The research utilizes the Long-Short-Term Memory (LSTM) method of artificial neural networks and Seasonal Auto Regressive Integrated Moving Average (SARIMA) to forecast future attenuation series after generating rain-induced attenuation using the Synthetic Storm Technique. Thirty-year rain datasets (1994 - 2023) were sourced from the South African weather station to generate time series attenuation. Three performance metric measures were used to evaluate the predictive ability of the SARIMA model with the LSTM: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results show that SARIMA was better at predicting rain-induced attenuation in subtropical areas than LSTM. This is because it had lower forecast performance error indicators (RMSE 1.42). The results will find their application in the digital transformation of global networks for planning 5G networks and beyond.