Forecasting with Mixed Frequencies

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Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, background): “crude oil inventories, and (5) in the Baltic Dry Index (BDI), (6) returns and excess returns on oil company stocks, (7) cumulative changes in U.”
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, background): “For example, in macroeconomic forecasting, extensive literature indicates that usingmonthly data to forecast quarterlymacro-economic variables (such as GDP, or inflation rate) will improve the accuracy of a forecast (Andreou et al., 2013; Armesto et al., 2010; Clements and Galvao, 2008, 2009; Ghysel”
State Space Models and MIDAS Regressions (2013, methodology): “(2)A user-friendly introduction to MIDAS regression models appearing in Armesto et al. (2010).”
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting (2022, background): “…recognized challenges of extremely long horizon predictions, and proposed mixed data sampling regression ( MIDAS ; Ghysels, Sinko, and Valkanov 2007; Armesto, Engemann, and Owyang 2010) to ameliorate the problem of parameter proliferation while preserving high-frequency temporal information.”