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Abstract Regression models (LEEMYR: Low Energy Electron MLT geosYnchronous orbit Regression) predict hourly 4.1–30 keV electron flux at geostationary orbit (GOES‐16) using solar wind, IMF, and geomagnetic index parameters. Multiplicative interaction and polynomial terms describe synergistic and nonlinear effects. We reduce predictors to an optimal set using stepwise regression, resulting in models with validation comparable to a neural network. Models predict 1, 3, 6, 12, and 24 hr into the future. Validation correlations are as high as 0.78 (4.1 and 11 keV, 1 hr prediction) and Heidke Skill scores (HSS) up to 0.66. A 3 hr ahead prediction is more practical, with slightly lower validation correlation (0.75) and HSS (0.61). The addition of location (MLT: magnetic local time) as a covariate, including multiplicative interaction terms, accounts for location‐dependent flux differences and variation of parameter influence, and allows prediction over the full orbit. Adding a substorm index (SME) provides minimal increase in validation correlation (0.81) showing that other parameters are good proxies for an unavailable real time substorm index. Prediction intervals on individual values provide more accurate assessments of model quality than confidence intervals on the mean values. An inverse N‐weighted least squares approach is impractical as it increases false positive warnings. Physical interpretations are not possible as spurious correlations due to common cycles are not removed. However,SME, Bz, Kp, and Dst are the highest correlates of electron flux, with solar wind velocity, density, and pressure, and IMF magnitude being less well correlated. 

Abstract Electron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p < 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence ofB_z), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters.