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 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.

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.