\n \uae08\uc735 \uc2dc\uc7a5\uc740 \ubcf5\uc7a1\ud558\uace0 \ub3d9\uc801\uc778 \ud658\uacbd\uc73c\ub85c, \ud22c\uc790\uc790\ub4e4\uc740 \uc9c0\uc18d\uc801\uc73c\ub85c \uc218\uc775\uc744 \ucd94\uad6c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\uc5d0 \ub530\ub77c
\n \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uae30\uc220 \uc911 \ud558\ub098\uc778 \ub2e8\uce35 \uc21c\uc804\ud30c \uc624\ud1a0\uc778\ucf54\ub354\ub97c
\n \uc774\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c \uad6c\ucd95 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\n <\/p>\n
\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21 \ub610\ub294 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4.
\n \ubc18\uba74\uc5d0 \ub525\ub7ec\ub2dd\uc740 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744
\n \uc778\uc2dd\ud569\ub2c8\ub2e4. \uc774 \ub450 \uae30\uc220\uc740 \ud2b9\ud788 \uace0\ucc28\uc6d0 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ube44\uc815\ud615 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0\uc11c \uc6b0\uc218\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n <\/p>\n
\n \uc624\ud1a0\uc778\ucf54\ub354\ub294 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc555\ucd95\ud558\uace0, \ubcf5\uc6d0\ud558\ub294 \ube44\uc9c0\ub3c4 \ud559\uc2b5 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc778\ucf54\ub354\uc640 \ub514\ucf54\ub354\ub85c \uad6c\uc131\ub418\uc5b4
\n \uc788\uc73c\uba70, \ub370\uc774\ud130\uc758 \uc911\uc694\ud55c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub2e8\uce35 \uc21c\uc804\ud30c \uc624\ud1a0\uc778\ucf54\ub354\ub294 \uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \ud615\ud0dc\ub85c, \ub2e8\uc77c
\n \uc740\ub2c9\uce35\uc744 \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \uc624\ud1a0\uc778\ucf54\ub354\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc131 \uc694\uc18c\ub4e4\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4:<\/p>\n
\n \ub2e8\uce35 \uc21c\uc804\ud30c \uc624\ud1a0\uc778\ucf54\ub354\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\ud604 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uacfc \uc720\uba85\ud55c \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc778
\n \ud150\uc11c\ud50c\ub85c\uc6b0(TensorFlow)\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n <\/p>\n
\n \uc8fc\uc2dd \ub370\uc774\ud130\uc640 \uac19\uc740 \uae08\uc735 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub294 \uacfc\uac70 \uac00\uaca9, \uac70\ub798\ub7c9 \ub4f1\uc744 \ud3ec\ud568\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
import pandas as pd\n\n# \uc8fc\uc2dd \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\nfeatures = data[['Open', 'High', 'Low', 'Close', 'Volume']].values<\/code><\/pre>\n4.2. \uc624\ud1a0\uc778\ucf54\ub354 \ubaa8\ub378 \uc815\uc758\ud558\uae30<\/h3>\n
\n \uac04\ub2e8\ud55c \ub2e8\uce35 \uc21c\uc804\ud30c \uc624\ud1a0\uc778\ucf54\ub354 \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n <\/p>\n
import tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Input, Dense\n\n# \ubaa8\ub378 \uad6c\uc131\ninput_layer = Input(shape=(features.shape[1],))\nencoded = Dense(32, activation='relu')(input_layer)\ndecoded = Dense(features.shape[1], activation='sigmoid')(encoded)\n\nautoencoder = Model(input_layer, decoded)\nautoencoder.compile(optimizer='adam', loss='mean_squared_error')<\/code><\/pre>\n4.3. \ubaa8\ub378 \ud6c8\ub828\uc2dc\ud0a4\uae30<\/h3>\n
\n \uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0b5\ub2c8\ub2e4.\n <\/p>\n
# \ubaa8\ub378 \ud6c8\ub828\nautoencoder.fit(features, features, epochs=50, batch_size=256, shuffle=True)<\/code><\/pre>\n5. \uacb0\uacfc \ud574\uc11d \ubc0f \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\n \ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4, \uc624\ud1a0\uc778\ucf54\ub354\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc6d0\ubcf8 \ub370\uc774\ud130\uc640 \ubcf5\uc6d0\ub41c \ub370\uc774\ud130\ub97c \ube44\uad50\ud558\uc5ec
\n \ubaa8\ub378\uc758 \uc815\ud655\uc131\uc744 \ud310\ub2e8\ud569\ub2c8\ub2e4.\n <\/p>\n
5.1. \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\n \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc9c1\uad00\uc801\uc73c\ub85c \uc774\ud574\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
import matplotlib.pyplot as plt\n\n# \uc608\uce21\npredicted = autoencoder.predict(features)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(features[0], label='Original')\nplt.plot(predicted[0], label='Reconstructed')\nplt.legend()\nplt.show()<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub2e8\uce35 \uc21c\uc804\ud30c \uc624\ud1a0\uc778\ucf54\ub354\ub97c \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ub97c \ub2e4\ub918\uc2b5\ub2c8\ub2e4.
\n \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc740 \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c \uc911\uc694\ud55c \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ud5a5\ud6c4 \uace0\uae09 \ubaa8\ub378\uc778 \ub2e4\uce35 \uc624\ud1a0\uc778\ucf54\ub354 \ubc0f LSTM \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \uc2ec\ud654 \uac15\uc88c\ub3c4 \uc900\ube44\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n <\/p>\n
7. \ucc38\uace0\ubb38\ud5cc<\/h2>\n