Abstract
Forecasting climate events is crucial for mitigating and managing risks related to climate change; however, the problem of non-stationarity in time series (NTS) arises, making it difficult to capture and model the underlying trends. This task requires a complex procedure to address the challenge of creating a strong model that can effectively handle the non-uniform variability in various climate datasets. In this work, we use a daily standardized precipitation index dataset as an example of NTS, whereby the heterogeneous variability of daily precipitation poses complexities for traditional machine-learning models in predicting future events. To address these challenges, we introduce a novel approach, aiming to adjust the non-uniform distribution and simplify the detection time lags using autocorrelation. Our study employs a range of statistical techniques, including sampling-based seasonality, mathematical transformation, and normalization, to preprocess the data to increase the time lag window. Through the exploration of linear and sinusoidal transformation, we aim to assess their impact on increasing the accuracy of forecasting models. A strong performance is effectively observed by using the proposed approach to capture more than one year of time delay across all the seasonal subsets. Furthermore, improved model accuracy is observed, notably with K-Nearest Neighbors (KNN) and Random Forest (RF). This study underscores RF’s consistently strong performance across all the transformations, while KNN only demonstrates optimal results when the data have been linearized.