LSTMʹΑΔ Dstࢦ ༧ଌ ࢀߟจ Gruet, Marina A., et al. "Multiple-Hour-Ahead Forecast of the Dst Index Using a Combination of Long Short- Term Memory Neural Network and Gaussian Process." Space Weather 16.11 (2018): 1882-1896.
with Dropout & ReLu Dst T+p Dst T−5 h ˜ Bz T−5 h ˜ N T−5 h ˜ V T−5 h ˜ |B| T−5 h Dst T−4 h ˜ Bz T−4 h ˜ N T−4 h ˜ V T−4 h ˜ |B| T−4 h Dst T−3 h ˜ Bz T−3 h ˜ N T−3 h ˜ V T−3 h ˜ |B| T−3 h Dst T−2 h ˜ Bz T−2 h ˜ N T−2 h ˜ V T−2 h ˜ |B| T−2 h Dst T−1 h ˜ Bz T−1 h ˜ N T−1 h ˜ V T−1 h ˜ |B| T−1 h Dst T ˜ Bz T ˜ N T ˜ V T ˜ |B| T : ଠཅ෩࣓ೆ͖ : ଠཅ෩࣓ڧ : ଠཅ෩ϓϥζϚີ : ଠཅ෩ Bz |B| N V ※ ཧྔ ͷ࣌ࠁ ͷฏۉ ˜ X T X T − 1 ∼ T ✓ p = 4 h ✓ Output dim : 125 (LSTM), 25(FC) ✓ Dropout ratio : 0.5 ✓ Batch size: 32 ✓ Epochs: 100 LSTM Cell LSTM Cell LSTM Cell LSTM Cell LSTM Cell Fully Connected Layer 2 with Dropout ݱࡏ࣌ࠁ ʹରͯ͠ɺ ࣌ؒޙͷ Dstࢦ Λ༧ଌ T p ࣮ݧύϥϝʔλ
0 + w 1 x + w 2 x2 + w 3 x3 = wTϕ(x) w = (w 0 , w 1 , w 2 , w 3 )T, ϕ(x) = (1, x, x2, x3)T جఈؔ ϕ h (x) = exp ( − (x − μ h )2 σ2 ) y = H ∑ h=−H w h exp ( − (x − μ h )2 σ2 ) ͷͱ͖جఈؔͷݸ 21 ݸɻ ͷ࣍ݩΛ 10࣍ݩʹ͢Δͱ? ɹˠ ݸ (࣍ݩͷढ͍) H = 10 x 2110 = 16,679,880,978,201 ΨεաఔʹΑΔղܾʂ
(2019)ʰΨεաఔͱػցֶशʱɹߨஊࣾ ✓ Gruet, Marina A., et al. "Multiple-Hour-Ahead Forecast of the Dst Index Using a Combination of Long Short-Term Memory Neural Network and Gaussian Process." Space Weather 16.11 (2018): 1882-1896. 31 ࢀߟࢿྉ σʔλऔಘݩ ✓ Dst ࢦ - ژେֶେֶӃཧֶݚڀՊଐ࣓ؾੈքࢿྉղੳηϯλʔ [http://wdc.kugi.kyoto-u.ac.jp/wdc/Sec3-j.html] ✓ ଠཅ෩σʔλ - High resolution OMNI (5-min) ͷଠཅ෩σʔλ [https://omniweb.gsfc.nasa.gov/ow_min.html] - Pyspedas [https://github.com/spedas/pyspedas] Λར༻ͯ͠μϯϩʔυ
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