Time series regression modeling on a dataset of supermarket sales across years, with the Darts library in Python.

Seasonal-Trend decomposition based on Loess + Machine Learning: Hybrid Forecasting for Monthly Univariate Time Series

This repo applies STLplus to GRACE/GRACE-FO TWSA data from CSR (Center for Space Research) with a 0.25-degree spatial resolution and a one-month temporal resolution. STLplus is an improved decomposition approach of seasonal and trend decomposition using Loess (STL) by Cleveland et al. (2019), developed by Ryan Hafen.

This project conducts signal decomposition on spatiotemporal data, such as hydrological data that varies spatially across grids over a specific period. The decomposition process is applied to terrestrial water storage anomaly (TWSA) data from the GRACE satellite mission.