{"id":29259,"date":"2024-10-27T16:17:33","date_gmt":"2024-10-27T16:17:33","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29259"},"modified":"2024-11-26T06:53:22","modified_gmt":"2024-11-26T06:53:22","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ec%9e%90%ea%b8%b0-%ec%83%81%ea%b4%80%ea%b3%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29259\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc790\uae30 \uc0c1\uad00\uacc4\uc218\uc758 \uce21\uc815"},"content":{"rendered":"

<\/p>\n

\n \ud604\ub300 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uc608\uce21\uc744 \ud1b5\ud55c \uc804\ub7b5\uc801 \uc758\uc0ac\uacb0\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ud2b9\ud788, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc911\uc694\uc131\uc774 \ub354\uc6b1 \ucee4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c \uac1c\ubc1c\uc5d0 \uc788\uc5b4 \uc790\uae30 \uc0c1\uad00\uacc4\uc218\uc758 \uce21\uc815 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac1c\ub150<\/h2>\n

\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \ud1b5\ud574 \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998\uc740 \ud2b9\uc815 \uc870\uac74\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \ubc1c\uc0dd\uc2dc\ud0a4\uba70, \uc774\ub294 \uc778\uac04\uc758 \uac10\uc815\uc774\ub098 \uc9c1\uad00\uc5d0 \uc758\uc874\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud2b9\uc131 \ub355\ubd84\uc5d0 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ube60\ub978 \uc758\uc0ac\uacb0\uc815\uacfc \uc2e4\ud589\uc774 \uac00\ub2a5\ud558\uba70, \ub300\ub7c9\uc758 \uac70\ub798\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

2. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h2>\n

2.1 \uba38\uc2e0\ub7ec\ub2dd \uac1c\uc694<\/h3>\n

\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uac10\ub3c5 \ud559\uc2b5, \ube44\uac10\ub3c5 \ud559\uc2b5, \uac15\ud654 \ud559\uc2b5 \ub4f1 \uc5ec\ub7ec \ud559\uc2b5 \ubc29\uc2dd\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\ub294 \uacfc\uac70 \uac00\uaca9 \ub370\uc774\ud130, \uac70\ub798\ub7c9, \uc7ac\ubb34 \uc81c\ud45c \ub4f1 \ub2e4\uc591\ud55c \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \ubbf8\ub798\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.\n<\/p>\n

2.2 \ub525\ub7ec\ub2dd\uc758 \ud2b9\uc9d5<\/h3>\n

\n \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud574 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \uc5ec\ub7ec \uce35\uc758 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub294 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \ub354\uc6b1 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ud2b9\ud788, \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub429\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \uae30\ubc95\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\ub3c4 \uc801\uc6a9\ub418\uc5b4, \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud30c\uc545\ud558\ub294 \ub370 \uae30\uc5ec\ud569\ub2c8\ub2e4.\n<\/p>\n

3. \uc790\uae30 \uc0c1\uad00\uacc4\uc218\uc758 \uc815\uc758\uc640 \uc911\uc694\uc131<\/h2>\n

\n \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub294 \uc2dc\uac04\uc5d0 \ub530\ub77c \ubcc0\ud654\ud558\ub294 \ub370\uc774\ud130 \uc2dc\ud000\uc2a4 \ub0b4\uc5d0\uc11c \uc790\uae30 \uc790\uc2e0\uacfc\uc758 \uc0c1\uad00 \uad00\uacc4\ub97c \uce21\uc815\ud558\ub294 \uc9c0\ud45c\uc785\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130\uac00 \uc2dc\uac04\uc5d0 \ub530\ub77c \uc5b4\ub5bb\uac8c \ubcc0\ud654\ud558\ub294\uc9c0\ub97c \ubd84\uc11d\ud558\ub294 \ub370 \uc720\uc6a9\ud558\uba70, \uc8fc\uc2dd \uac00\uaca9\uc774\ub098 \uac70\ub798\ub7c9\uacfc \uac19\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \uc790\uc8fc \ud65c\uc6a9\ub429\ub2c8\ub2e4. \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \uce21\uc815\ud568\uc73c\ub85c\uc368 \ubc18\ubcf5\ub418\ub294 \ud328\ud134\uc774\ub098 \ucd94\uc138\ub97c \ucc3e\uc544\ub0bc \uc218 \uc788\uc73c\uba70, \uc774\ub294 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5 \uc218\ub9bd\uc5d0 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.\n<\/p>\n

3.1 \uc790\uae30 \uc0c1\uad00\uacc4\uc218\uc758 \uacc4\uc0b0 \ubc29\ubc95<\/h3>\n

\n \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4:\n<\/p>\n

\n    autocorr(x, lag) = Cov(x_t, x_(t-lag)) \/ Var(x)\n<\/code><\/pre>\n
\n    \uc5ec\uae30\uc11c Cov<\/code>\ub294 \uacf5\ubd84\uc0b0, Var<\/code>\ub294 \ubd84\uc0b0\uc744 \uc758\ubbf8\ud558\uba70, x_t<\/code>\ub294 \uc2dc\uc810 t<\/code>\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \uac12\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. lag<\/code>\ub294 \uc2dc\uac04 \uc9c0\uc5f0\uc744 \ub098\ud0c0\ub0b4\uba70, \uba87 \uac1c\uc758 \uc2dc\uc810 \uc774\uc804\uc758 \ub370\uc774\ud130\uc640\uc758 \uc0c1\uad00 \uad00\uacc4\ub97c \uce21\uc815\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, lag=1<\/code>\uc77c \ub54c\ub294 \ud604\uc7ac \uac12\uacfc \ubc14\ub85c \uc774\uc804 \uac12\uc744 \ube44\uad50\ud558\uac8c \ub429\ub2c8\ub2e4.\n<\/p>\n
4. \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \uc801\uc6a9 \uc608\uc81c<\/h2>\n
\n    \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc2e4\uc81c \uc608\uc81c\ub97c \uc0b4\ud3b4\ubd05\uc2dc\ub2e4. \uc6b0\ub9ac\ub294 \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \ud65c\uc6a9\ud558\uc5ec \uacfc\uac70\uc758 \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798\uc758 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
4.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\n    \uac00\uaca9 \ub370\uc774\ud130\ub294 Yahoo Finance\uc640 \uac19\uc740 API\ub97c \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Python\uc758 pandas_datareader<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uac00\uc838\uc635\ub2c8\ub2e4.\n<\/p>\n
\nimport pandas as pd\nimport pandas_datareader.data as web\nfrom datetime import datetime\n\n# \ub370\uc774\ud130 \uc218\uc9d1\nstart = datetime(2020, 1, 1)\nend = datetime(2023, 1, 1)\nstock_data = web.DataReader('AAPL', 'yahoo', start, end)\n<\/code><\/pre>\n
4.2 \uc790\uae30 \uc0c1\uad00\uacc4\uc218 \uacc4\uc0b0\ud558\uae30<\/h3>\n
\nstatsmodels<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\uace0 \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \uad6c\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nimport statsmodels.api as sm\n\n# \uc885\uac00 \ub370\uc774\ud130 \ucd94\ucd9c\nclose_prices = stock_data['Close']\n\n# \uc790\uae30 \uc0c1\uad00\uacc4\uc218 \uacc4\uc0b0\nautocorr = sm.tsa.acf(close_prices, nlags=30)\nprint(autocorr)\n<\/code><\/pre>\n
4.3 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\n    \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \uae30\ubc18\uc73c\ub85c \uc785\ub825 \ud53c\ucc98\ub97c \uc0dd\uc131\ud558\uace0, \uc774\ub97c \uc0ac\uc6a9\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uaca0\uc2b5\ub2c8\ub2e4. Scikit-Learn\uc758 LinearRegression<\/code>\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# \ud53c\ucc98 \uc0dd\uc131\nX = []\ny = []\nfor i in range(30, len(close_prices)):\n    X.append(autocorr[i-30:i])\n    y.append(close_prices[i])\n\nX = pd.DataFrame(X)\ny = pd.Series(y)\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n
4.4 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\n    \ubaa8\ub378 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 MSE(Mean Squared Error)\uc640 R\u00b2(R-squared) \uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \uc131\ub2a5 \ud3c9\uac00\nmse = mean_squared_error(y_test, y_pred)\nr_squared = r2_score(y_test, y_pred)\n\nprint(f\"MSE: {mse}, R\u00b2: {r_squared}\")\n<\/code><\/pre>\n
5. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc801\uc6a9 \uc608\uc81c<\/h2>\n
\n    \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec LSTM(Long Short-Term Memory) \ubaa8\ub378\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4.\n<\/p>\n
5.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n    LSTM \ubaa8\ub378\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ub370\uc774\ud130\ub97c \uc7ac\uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\uace0 \uc0d8\ud50c\uc758 \ud615\uc2dd\uc744 \ub9de\ucdb0 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.preprocessing import MinMaxScaler\nimport numpy as np\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nscaler = MinMaxScaler()\nscaled_data = scaler.fit_transform(close_prices.values.reshape(-1, 1))\n\n# \uc0d8\ud50c \ub370\uc774\ud130 \uc0dd\uc131\nX_lstm, y_lstm = [], []\nfor i in range(30, len(scaled_data)):\n    X_lstm.append(scaled_data[i-30:i])\n    y_lstm.append(scaled_data[i, 0])\n\nX_lstm = np.array(X_lstm)\ny_lstm = np.array(y_lstm)\n<\/code><\/pre>\n
5.2 LSTM \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Dropout\n\n# LSTM \ubaa8\ub378 \uc0dd\uc131\nmodel_lstm = Sequential()\nmodel_lstm.add(LSTM(units=50, return_sequences=True, input_shape=(X_lstm.shape[1], 1)))\nmodel_lstm.add(Dropout(0.2))\nmodel_lstm.add(LSTM(units=50, return_sequences=True))\nmodel_lstm.add(Dropout(0.2))\nmodel_lstm.add(LSTM(units=50))\nmodel_lstm.add(Dropout(0.2))\nmodel_lstm.add(Dense(units=1))  # \uc608\uce21\ud560 \uac12\uc740 \uc8fc\uac00\uc758 \uc885\uac00\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel_lstm.compile(optimizer='adam', loss='mean_squared_error')\n<\/code><\/pre>\n
5.3 \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00<\/h3>\n
\n# \ubaa8\ub378 \ud559\uc2b5\nmodel_lstm.fit(X_lstm, y_lstm, epochs=100, batch_size=32)\n\n# \uc608\uce21\ntrain_predict = model_lstm.predict(X_lstm)\n\n# \uc2a4\ucf00\uc77c \ubcf5\uc6d0\ntrain_predict = scaler.inverse_transform(train_predict)\noriginal_data = scaler.inverse_transform(scaled_data[30:])\n\n# \uc131\ub2a5 \ud3c9\uac00\nmse = mean_squared_error(original_data, train_predict)\nprint(f\"LSTM MSE: {mse}\")\n<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\n    \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uc608\uce21 \ubc29\ubc95\uc73c\ub85c \ube60\ub974\uac8c \uc790\ub9ac \uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub294 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \uc774\ud574\ud558\ub294 \ub370 \uc911\uc694\ud55c \ub3c4\uad6c\ub85c \ud65c\uc6a9\ub429\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud1b5\ud574 \uc790\uae30 \uc0c1\uad00\uacc4\uc218\ub97c \uc774\uc6a9\ud55c \uac00\uaca9 \uc608\uce21 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\ubc95\ub860\ub4e4\uc744 \uc798 \ud65c\uc6a9\ud558\uba74, \ub354\uc6b1 \uc815\uad50\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
\ucc38\uace0 \ubb38\ud5cc<\/h2>\n