As an important proxy of the solar extreme ultraviolet radiation from the upper chromosphere and lower corona, the 10.7 cm solar radio flux (F10.7) has a wide range of applications in models of the thermosphere and ionosphere. Forecasting F10.7 has already become a routine business in space weather services. In this study, we analyzed the predictive power of autoregressive (AR) models with orders p=15\documentclass[12pt]{minimal}
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\begin{document}$p=15$\end{document} – 1005, a training sample length L=22\documentclass[12pt]{minimal}
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\begin{document}$L=22$\end{document} years, and a running time window w=50\documentclass[12pt]{minimal}
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\begin{document}$w = 50$\end{document} days on the daily F10.7, during the last two solar cycles (Solar Cycles 23 and 24) at the forecast steps n=1\documentclass[12pt]{minimal}
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\begin{document}$n=1$\end{document} – 81 days. The main conclusions are as follows. (i) The mean forecast error (δ‾\documentclass[12pt]{minimal}
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\begin{document}$\overline{\delta }$\end{document}) at the n\documentclass[12pt]{minimal}
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\begin{document}$n$\end{document}th day or over N\documentclass[12pt]{minimal}
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\begin{document}$N$\end{document} days is minimum at an optimal order po\documentclass[12pt]{minimal}
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\begin{document}$p_{\mathrm{o}}$\end{document}, which tends to increase as n\documentclass[12pt]{minimal}
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\begin{document}$n$\end{document} or N\documentclass[12pt]{minimal}
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\begin{document}$N$\end{document} increases. (ii) δ‾\documentclass[12pt]{minimal}
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\begin{document}$\overline{\delta }$\end{document} is positively related to both n\documentclass[12pt]{minimal}
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\begin{document}$n$\end{document} and F10.7. The large error during the maximum period is the result of the large daily variation in F10.7, mainly due to the appearance and decay of active regions, especially the eruptions of solar flares. (iii) The solar cycle can be divided into six parts in the rising order of δ‾\documentclass[12pt]{minimal}
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\begin{document}$\overline{\delta }$\end{document}: (a) closing part of the declining phase, (b) initial rising phase, (c) middle declining phase, (iv) closing rising phase, (v) middle rising phase, and (f) initial declining phase. (iv) The AR model at po\documentclass[12pt]{minimal}
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\begin{document}$p_{\mathrm{o}}$\end{document} is not inferior to other techniques. (v) po\documentclass[12pt]{minimal}
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\begin{document}$p_{\mathrm{o}}$\end{document} is uncorrelated to the autocorrelation coefficient, and (vi) δ‾\documentclass[12pt]{minimal}
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\begin{document}$\overline{\delta }$\end{document} is minimum at a certain L\documentclass[12pt]{minimal}
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\begin{document}$L$\end{document}.
