\ucd5c\uadfc \uba87 \ub144\uac04 \ube44\ud2b8\ucf54\uc778\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c \uac00\uc7a5 \uc8fc\ubaa9\ubc1b\ub294 \uc790\uc0b0\uc73c\ub85c, \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc774 \uc774\ub97c \ud65c\uc6a9\ud558\uc5ec \uc218\uc775\uc744 \ucd94\uad6c\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ube44\ud2b8\ucf54\uc778 \uac00\uaca9\uc740 \ub9e4\uc6b0 \ubcc0\ub3d9\uc131\uc774 \ud06c\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \uc608\uce21\ud558\ub294 \uac83\uc740 \uc0c1\ub2f9\ud788 \uc5b4\ub824\uc6b4 \uacfc\uc81c\uac00 \ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc758 \ud55c \ubc29\ubc95\uc778 LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\ub97c \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc2dc\uac04 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub294 \ud2b9\uc815 \uc2dc\uac04\uc5d0 \uac01 \ubcc0\uc218\uc758 \uac12\uc744 \uae30\ub85d\ud55c \ub370\uc774\ud130\ub85c, \uc77c\ubc18\uc801\uc73c\ub85c \uc2dc\uac04\uc758 \ud750\ub984\uc5d0 \ub530\ub77c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4. \uc989, \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9, \uac70\ub798\ub7c9 \ub4f1\uc758 \ub370\uc774\ud130\ub294 \uc2dc\uac04\uc5d0 \ub530\ub77c \ubcc0\ud654\ud558\uba70, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21 \ubc0f \ubd84\uc11d\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \uc608\ub85c\ub294 \uc8fc\uc2dd \uac00\uaca9, \ub0a0\uc528 \uc815\ubcf4, \ub9e4\ucd9c \ub370\uc774\ud130 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\ub294 RNN(Recurrent Neural Network)\uc758 \ud55c \uc885\ub958\ub85c, \uc21c\ud658 \uc2e0\uacbd\ub9dd\uc758 \ubb38\uc81c\uc810\uc778 \uc7a5\uae30 \uc758\uc874\uc131 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. LSTM\uc740 \ub0b4\ubd80\uc5d0 \uae30\uc5b5 \uc140\uc744 \ub450\uc5b4 \uc815\ubcf4\ub97c \uc624\ub79c \uc2dc\uac04 \ub3d9\uc548 \uc800\uc7a5\ud560 \uc218 \uc788\uc73c\uba70, \ub2e4\uc74c\uacfc \uac19\uc740 \uc138 \uac00\uc9c0 \uc8fc\uc694 \uac8c\uc774\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc815\ubcf4\ub97c \uc870\uc808\ud569\ub2c8\ub2e4.<\/p>\n
\uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 LSTM\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc774 \ud504\ub85c\uc138\uc2a4\ub97c \uc9c4\ud589\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n
\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\ub294 \uc5ec\ub7ec API\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c CryptoCompare, Binance, CoinGecko \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 Pandas\uc640 NumPy\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\nimport numpy as np\n\n# Binance API\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc218\uc9d1 \uc608\uc81c\ndef fetch_data(symbol='BTCUSDT', interval='1d', limit=1000):\n url = f'https:\/\/api.binance.com\/api\/v3\/klines?symbol={symbol}&interval={interval}&limit={limit}'\n df = pd.read_json(url)\n df = df[[0, 4]].rename(columns={0: 'timestamp', 4: 'close_price'})\n df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n return df\n\n# \ub370\uc774\ud130 \ub2e4\uc6b4\ub85c\ub4dc\ndf = fetch_data()\nprint(df.head())\n <\/code><\/pre>\n3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ubaa8\ub378 \ud559\uc2b5\uc758 \uc6a9\ub3c4\ub85c \uc801\ud569\ud558\uac8c \uac00\uacf5\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc6b0\ub9ac\uac00 \ud544\uc694\ud55c \uac83\uc740 ‘\uc815\uaddc\ud654’\uc785\ub2c8\ub2e4. LSTM \ubaa8\ub378\uc740 \uc785\ub825 \uac12\uc774 \uc791\uc740 \ubc94\uc704\uc5d0 \uc788\uc744 \ub54c \ub354 \uc798 \ub3d9\uc791\ud558\uae30 \ub54c\ubb38\uc5d0, Min-Max \uc815\uaddc\ud654 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h4>\n\nfrom sklearn.preprocessing import MinMaxScaler\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nscaler = MinMaxScaler(feature_range=(0, 1))\ndf['scaled_close'] = scaler.fit_transform(df['close_price'].values.reshape(-1, 1))\n\n# \ub370\uc774\ud130 \ubd84\ud560\ntrain_size = int(len(df) * 0.8)\ntrain_data = df['scaled_close'][:train_size]\ntest_data = df['scaled_close'][train_size:]\n\n# \uc2dc\ud000\uc2a4 \uc0dd\uc131\ndef create_dataset(data, time_step=1):\n X, Y = [], []\n for i in range(len(data) - time_step - 1):\n X.append(data[i:(i + time_step)])\n Y.append(data[i + time_step])\n return np.array(X), np.array(Y)\n\ntime_step = 10\nX_train, y_train = create_dataset(train_data.values, time_step)\nX_test, y_test = create_dataset(test_data.values, time_step)\n\n# \uc785\ub825 \ub370\uc774\ud130 \ucc28\uc6d0 \uc870\uc815\nX_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\nX_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n <\/code><\/pre>\n3.3 LSTM \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud559\uc2b5<\/h3>\n
\uc774\uc81c LSTM \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud559\uc2b5\ud569\ub2c8\ub2e4. Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec LSTM \ubaa8\ub378\uc744 \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: LSTM \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud559\uc2b5<\/h4>\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout\n\n# LSTM \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(units=50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(units=1)) # \ucd9c\ub825\uce35\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train, y_train, epochs=50, batch_size=32)\n <\/code><\/pre>\n3.4 \uc608\uce21 \ubc0f \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc608\uce21\ud55c \ud6c4, \uadf8 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: \uc608\uce21 \ubc0f \uc2dc\uac01\ud654<\/h4>\n\nimport matplotlib.pyplot as plt\n\n# \uc608\uce21 \uc218\ud589\ntrain_predict = model.predict(X_train)\ntest_predict = model.predict(X_test)\n\n# \ub370\uc774\ud130 \uc2a4\ucf00\uc77c \ub418\ub3cc\ub9ac\uae30\ntrain_predict = scaler.inverse_transform(train_predict)\ntest_predict = scaler.inverse_transform(test_predict)\n\n# \uc2dc\uac01\ud654\nplt.figure(figsize=(14, 5))\nplt.plot(df['timestamp'][:train_size], scaler.inverse_transform(train_data.values[time_step:-1]), label='Train Data', color='blue')\nplt.plot(df['timestamp'][train_size + time_step:-1], scaler.inverse_transform(test_data.values[time_step:-1]), label='Test Data', color='orange')\nplt.plot(df['timestamp'][time_step:train_size], train_predict, label='Train Predict', color='red')\nplt.plot(df['timestamp'][train_size + time_step:], test_predict, label='Test Predict', color='green')\nplt.legend()\nplt.show()\n <\/code><\/pre>\n4. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h2>\n
\ubaa8\ub378\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc740 \uc608\uce21\uc758 \uc815\ud655\uc131\uc744 \ub192\uc774\uace0, \ud544\uc694\ud55c \uacbd\uc6b0 \uac1c\uc120 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ubaa8\ub378\uc5d0\uc11c \uc608\uce21\ud55c \ub370\uc774\ud130\uc640 \uc2e4\uc81c \ub370\uc774\ud130\uc758 \ucc28\uc774\ub97c \uacc4\uc0b0\ud558\uc5ec RMSE(Root Mean Squared Error)\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608\uc81c \ucf54\ub4dc: RMSE \uacc4\uc0b0<\/h4>\n\nfrom sklearn.metrics import mean_squared_error\n\n# RMSE \uacc4\uc0b0\ntrain_rmse = np.sqrt(mean_squared_error(scaler.inverse_transform(train_predict), scaler.inverse_transform(train_data.values[time_step:-1])))\ntest_rmse = np.sqrt(mean_squared_error(scaler.inverse_transform(test_predict), scaler.inverse_transform(test_data.values[time_step:-1])))\nprint(f'Train RMSE: {train_rmse}, Test RMSE: {test_rmse}')\n <\/code><\/pre>\n5. \ucd94\uac00\uc801\uc778 \uace0\ub824\uc0ac\ud56d<\/h2>\n
\ubaa8\ub378 \uad6c\ucd95 \ud6c4\uc5d0\ub294 \ucd94\uac00\uc801\uc778 \uace0\ub824\uc0ac\ud56d\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ud2b9\uc131\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \ubaa8\ub378 \ubcf5\uc7a1\ub3c4\uc758 \uc870\uc808, \ub370\uc774\ud130 \uc218\uc9d1 \ubc29\ubc95 \ub4f1\uc5d0 \ub530\ub77c \uc131\ub2a5\uc774 \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \ud301\uc785\ub2c8\ub2e4.<\/p>\n
\n- \ub370\uc774\ud130 \uc99d\uac15(Data Augmentation): \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0, \ub2e4\uc591\ud55c \uc8fc\uae30\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub354 \ub9ce\uc740 \ud2b9\uc131\uc744 \ubaa8\ub378\uc5d0 \uc81c\uacf5\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815: LSTM\uc758 \uc720\ub2db \uc218, \ud559\uc2b5\ub960 \ub4f1\uacfc \uac19\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud558\uc5ec \ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/li>\n
- \ubc30\uce58 \uc815\uaddc\ud654(Batch Normalization): LSTM \ub808\uc774\uc5b4 \uc804\uc5d0 \ubc30\uce58 \uc815\uaddc\ud654\ub97c \ucd94\uac00\ud558\uc5ec \ud559\uc2b5 \uc18d\ub3c4\ub97c \uc99d\uac00\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc559\uc0c1\ube14 \ud559\uc2b5: \uc5ec\ub7ec \ubaa8\ub378\uc744 \uc870\ud569\ud558\uc5ec \uc608\uce21\uc758 \uc2e0\ub8b0\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
6. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 LSTM\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. LSTM\uc740 \uc7a5\uae30 \uc758\uc874\uc131 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uc5ec \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21\uc758 \uc815\ud655\uc131\uc744 \ub192\uc77c \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ubaa8\ub378\uc744 \uc798 \uc124\uacc4\ud558\uace0, \uc801\uc808\ud788 \uac1c\uc120\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ucd94\uac00\uc801\uc778 \uc5f0\uad6c\uc640 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \uc774\ub04c\uc5b4 \ub0bc \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4\ub97c \uc704\ud55c \ub354\uc6b1 \ubc1c\uc804\ub41c \uc804\ub7b5\uc740 LSTM \uc678\uc5d0\ub3c4 \uc5ec\ub7ec \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\ub4e4\uc744 \uc870\ud569\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, CNN(Convolutional Neural Network)\uacfc \ud568\uaed8 \uc0ac\uc6a9\ud558\uc5ec \uac00\uaca9\uc758 \ud328\ud134\uc744 \uc778\uc2dd\ud558\uac70\ub098, \uac15\ud654\ud559\uc2b5(RL)\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \ub9e4\ub9e4 \ud0c0\uc774\ubc0d\uc744 \ucc3e\ub294 \ubc29\ubc95\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \ubcf5\uc7a1\uc131\uc744 \uace0\ub824\ud560 \ub54c, \uc774\ub7ec\ud55c \ub2e4\uc591\ud55c \uc811\uadfc \ubc29\uc2dd\ub4e4\uc740 \ub354\uc6b1 \ub9ce\uc740 \uc774\uc810\uc744 \uac00\uc838\ub2e4 \uc904 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n