Commodity　futures　are　an　important　hedging　tool　in　material　trade,　and　by　accurately　predicting　prices,　countries　and　firms　are　able　to　make　informed　production　and　consumption　decisions.　This　paper　introduces　a　novel　machine　learning　ensemble　method　that　combines　decomposition　algorithms　and　physical　optimization　algorithms　to　predict　commodity　futures　prices.　First,　the　VMD(Variational　mode　decomposition)　is　optimized　by　the　RIME　algorithm　(Rime　optimization　algorithm)　to　obtain　the　optimal　modal　decomposition　results,　and　the　trend　and　seasonal　terms　are　predicted　using　the　ELM　(Extreme　Learning　Machines)　and　FA　(Fourier　Attention)　models,　respectively,　and　the　results　are　finally　synthesized.　The　results　show　that　the　MAPE(mean　absolute　percentage　error)　of　one-step,　three-step,　and　six-step　methods　for　predicting　crude　oil　prices　are　0.48%,　0.66%,　and　0.75%,　respectively,　and　the　MAPE　of　soybean　prediction　results　are　0.22%,　0.27%,　and　0.37%,　respectively.　The　empirical　results　and　ablation　experiments　show　that　it　outperforms　other　benchmark　models　in　terms　of　both　horizontal　and　directional　accuracy.　Notably,　it　outperforms　in　predicting　soybean　futures　prices,　which　demonstrates　the　ability　of　our　model　to　better　capture　the　characteristics　of　both　the　time　and　frequency　domains　of　the　series,　to　take　sufficient　consideration　of　the　series　characteristics,　and　to　ensure　robustness.