Iron　ore　has　had　a　highly　global　market　since　setting　a　new　pricing　mechanism　in　2008.　With　current　dollar　values,　iron　ore　concentrate　for　sale　price,　which　was　$39　per　tonne　(62%　Fe)　in　December　2015,　reached　$218　per　tonne　(62%　Fe)　in　mid-2021.　It　is　hovering　around　$120　in　October　2023　(cf.　https://tradingeconomics.com/commodity/iron-ore).　The　uncertainty　associated　with　these　fluctuations　creates　hardship　for　iron　ore　mine　operators　and　steelmakers　in　planning　mine　development　and　making　future　sale　agreements.　Therefore,　iron　ore　price　forecasting　is　of　special　importance.　This　paper　proposes　a　cutting-edge　multi-echelon　tandem　learning　(METL)　model　to　forecast　iron　ore　prices.　This　model　comprises　variational　mode　decomposition　(VMD),　multi-head　convolutional　neural　network　(MCNN),　stacked　long　short-term-memory　(SLSTM)　network,　and　attention　mechanism　(AT).　In　the　proposed　METL　(i.e.,　the　combination　of　VMD,　MCNN,　SLSTM,　AT)　model,　the　VMD　decomposes　the　time　series　data　into　sub-sequential　modes　for　better　measuring　volatility.　Then,　the　MCNN　is　applied　as　an　encoder　to　extract　spatial　features　from　the　decomposed　sub-sequential　modes.　The　SLSTM　network　is　adopted　as　a　decoder　to　extract　temporal　features.　Finally,　the　AT　is　employed　to　capture　spatial–temporal　features　to　obtain　the　complete　forecasting　process.　Extensive　computational　experiments　are　conducted　based　on　daily-based　and　weekly-based　iron　ore　price　datasets　with　different　time　scales.　It　was　validated　that　the　proposed　METL　model　outperformed　its　single-echelon　and　other　categorized　models　by　10–65%　in　range.　The　proposed　METL　model　can　improve　the　prediction　accuracy　of　iron　ore　prices　and　thus　help　mining　and　steelmaking　enterprises　to　determine　their　sale　or　purchase　strategies.　©　International　Association　for　Mathematical　Geosciences　2024.