stlTDNN
STL Decomposition and TDNN Hybrid Time Series Forecasting
Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | stlTDNN_0.1.0.tar.gz |
14.2 KiB |
0.1.0 |
rolling linux/noble R-4.5 | stlTDNN_0.1.0.tar.gz |
14.1 KiB |
0.1.0 |
rolling source/ R- | stlTDNN_0.1.0.tar.gz |
4.4 KiB |
0.1.0 |
latest linux/jammy R-4.5 | stlTDNN_0.1.0.tar.gz |
14.2 KiB |
0.1.0 |
latest linux/noble R-4.5 | stlTDNN_0.1.0.tar.gz |
14.1 KiB |
0.1.0 |
latest source/ R- | stlTDNN_0.1.0.tar.gz |
4.4 KiB |
0.1.0 |
2026-04-26 source/ R- | stlTDNN_0.1.0.tar.gz |
4.4 KiB |
0.1.0 |
2026-04-23 source/ R- | stlTDNN_0.1.0.tar.gz |
4.4 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | stlTDNN_0.1.0.zip |
17.6 KiB |
0.1.0 |
2025-04-20 source/ R- | stlTDNN_0.1.0.tar.gz |
4.4 KiB |