20250731 meeting

Published 2025-07-30 Updated 2025-09-21 361 words 2 minutes views

Contents

Introduction

This section revises Experiments 2 and 3 from the previous study, so that the autoFRK model also predicts the known locations, rather than only the missing ones. The results are as follows:

Experiment 2

gallery_made_with_nanogallery_exp2

Method	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	122.088806	123.208755	117.288971	122.455757	121.838974	125.099060	122.074120	123.263573	116.976578
RMSPE	11.049380	11.099944	10.830004	11.065973	11.038070	11.184769	11.048716	11.102413	10.815571
MAPE	8.474641	8.495713	8.384321	8.760569	8.725639	8.910264	8.463192	8.486518	8.363283
MSPE%	0.434309	0.438404	0.416762	0.429368	0.427416	0.437735	0.434507	0.438843	0.415924
RMSPE%	0.659021	0.662121	0.645571	0.655262	0.653770	0.661615	0.659172	0.662452	0.644921
MAPE%	0.030226	0.030313	0.029852	0.030770	0.030664	0.031227	0.030204	0.030299	0.029797

Experiment 3

gallery_made_with_nanogallery_exp3

Method	ALL Locs & All Time	Known Locs & All Time	Unknown Locs & All Time	ALL Locs & Future	Known Locs & Future	Unknown Locs & Future	ALL Locs & Past	Known Locs & Past	Unknown Locs & Past
MSPE	17.35709501	17.18139008	18.11011615	24.97269111	24.97548082	24.96073520	17.05247116	16.86962645	17.83609138
RMSPE	4.16618471	4.14504404	4.25559821	4.99726836	4.99754748	4.99607198	4.12946379	4.10726508	4.22327970
MSPE%	0.06271946	0.06212193	0.06528030	0.08875877	0.08882456	0.08847681	0.06167789	0.06105383	0.06435244
RMSPE%	0.25043854	0.24924272	0.25550009	0.29792410	0.29803450	0.29745052	0.24835033	0.24709072	0.25367782
MAPE	3.23188170	3.21607107	3.29964151	4.06021896	4.05973676	4.06228551	3.19874821	3.18232445	3.26913575
MAPE%	0.01160292	0.01155222	0.01182024	0.01437071	0.01437764	0.01434101	0.01149221	0.01143920	0.01171941

Conclusion

Compared to Experiment 2, Experiment 3 first applies SSSDS4 and then autoFRK, resulting in a more conservative imputation outcome.

Epilogue

https://raw.githubusercontent.com/Josh-test-lab/website-assets-repository/refs/heads/main/posts/1140722%20meeting/To%20be%20continued.jpg — To be continued!

Environment

Local Operating System: Windows 11 24H2
- Programming Language: Python 3.12.9
Computing Platform: National Center for High-Performance Computing (NCHC) – Taiwan AI Cloud
- Operating System: Ubuntu
- Miniconda
- GPU: NVIDIA Tesla V100 32GB GPU
- CUDA 12.8 driver
- Programming Language: Python 3.10.16 for Linux

Further Learning

I tested this project on the GitHub repository.

References

Juan Lopez Alcaraz, Nils Strodthoff. (2022). Diffusion-based time series imputation and forecasting with structured state space models. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=hHiIbk7ApW
SSSD. (2022). GitHub. Retrieved from https://github.com/AI4HealthUOL/SSSD
SSSD_CP. (2024). GitHub. Retrieved from https://github.com/egpivo/SSSD_CP
近兩年小時值查詢. (n.d.). 環境部 - 空氣品質監測網. Retrieved from https://airtw.moenv.gov.tw/CHT/Query/InsValue.aspx