# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 1 Introduction
Traditional time-series forecasting methods, including ARIMA (Autoregressive Integrated Moving Average) and Exponential Smoothing, have been widely applied to data structured around the Gregorian Calendar [24; 25; 26]. The forecasting time interval is adjusted to meet the needs of the research, whether daily, monthly, or yearly. The performance of these statistical models is evaluated using various metrics, such as MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) [27; 28]. Forecasting using the Gregorian calendar often involves machine learning models to enhance accuracy. These models can help identify seasonal patterns and trends that may not be immediately apparent [29; 30]. Models such as LSTM and Bi-LSTM are commonly used for forecasting based on the Gregorian calendar [31; 32; 33]. Additionally, the GRU and Bi-GRU models have been shown to refine the parameters of the LSTM model for improved forecasting performance [34; 35]. However, forecasting using the Gregorian calendar often yields less accurate results because it does not always align with specific seasonal patterns. One of the main reasons is the incompatibility between the annual cycle of the Gregorian calendar and natural cycles that may follow different patterns, such as the lunar cycle or local climate variations [15]. Additionally, the Gregorian calendar may not always reflect significant seasonal fluctuations [36]. Therefore, forecasting models that rely on the Gregorian calendar should be adjusted or combined with more suitable calendars to improve the accuracy of predictions [37]. The lunar calendar can be used for time-series data modeling, but its application remains limited among researchers. Many prefer the Gregorian calendar because it is more widely used and supported by various analysis software [38]. While the lunar calendar has significant potential to capture unique seasonal patterns, few studies have explored its advantages [39]. Further research is needed to develop more effective methods for utilizing the lunar calendar. In many cultures, especially in parts of Asia, the lunar calendar plays a crucial role in religious and cultural practices. In some regions, climatic phenomena such as monsoons and droughts follow lunar cycles. The use of lunar calendars in various software, such as R, is still limited. Currently, there is no custom conversion for the lunar calendar months, similar to the built-in functions for the Gregorian calendar [40]. This limitation makes data analysis using the lunar calendar more complicated. Users must either perform manual conversions or rely on additional tools to support monthly calendar-based analysis. The development of an automatic conversion feature for the lunar calendar in R is urgently needed to simplify time-series data forecasting. Machine learning methods for forecasting have not been widely applied to the lunar calendar. Most research and machine learning applications still focus on the Gregorian calendar [41; 42; 43]. One of the main obstacles is the lack of datasets that support the lunar calendar. Additionally, existing machine learning algorithms have not been fully adapted for conversion to the lunar months. Therefore, further research is needed to develop more effective methods of lunar calendar-based forecasting. The urgency of this research arises from the need to improve the accuracy of rainfall forecasting, which has a wide range of applications, including flood prediction, disaster mitigation, and more. To address precipitation forecasting while incorporating the lunar calendar, we propose machine learning-based solutions that integrate historical precipitation data with lunar calendar information to enhance forecast accuracy. The objective of this research is to model and predict rainfall rates using various machine learning models based on the lunar calendar and compare these results with forecasts generated using the Gregorian calendar.