sufficientForecasting

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) doi:10.1016/j.jeconom.2017.08.009, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) doi:10.1093/biomet/asab037 and Yu, X., Yao, J. and Xue, L. (2022) doi:10.1080/07350015.2020.1813589.

Last 30 days

This package has been downloaded 194 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 2,715 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 29 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.