Recent research highlights the increasing use of transformer-based models, including PatchTST and Time-LLM, for time series forecasting, particularly in climate research and carbon emissions prediction. A significant challenge in this field is the prevalence of missing climate data, necessitating robust imputation techniques to ensure accurate analysis and mitigate risks associated with climate change. Various methods, ranging from statistical approaches like median imputation and multiple imputation to more advanced techniques like generative adversarial networks, are being explored to fill these gaps and improve the reliability of climate models.
Read the full article at Towards AI - Medium
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