{"id":31401,"date":"2024-10-28T12:01:46","date_gmt":"2024-10-28T12:01:46","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31401"},"modified":"2024-11-26T06:45:16","modified_gmt":"2024-11-26T06:45:16","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%ec%8b%9c%ea%b3%84%ec%97%b4-%ec%98%88%ec%b8%a1","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31401\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uc2dc\uacc4\uc5f4 \uc608\uce21 \ubaa8\ub378 ARIMA  ARIMA \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4 \uc608\uce21."},"content":{"rendered":"<p><body><\/p>\n<p>\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ube44\ud2b8\ucf54\uc778\uc740 \uae09\uaca9\ud55c \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ubcf4\uc774\uba70 \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc758 \uad00\uc2ec\uc744 \ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc774 \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 ARIMA(AutoRegressive Integrated Moving Average) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f9\ub2c8\ub2e4.<\/p>\n<h2>1. ARIMA \ubaa8\ub378 \uac1c\uc694<\/h2>\n<p>ARIMA \ubaa8\ub378\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. ARIMA\ub294 \ub2e4\uc74c\uc758 \uc138 \uac00\uc9c0 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>AR (\uc790\uae30\ud68c\uadc0) \ubd80\ubd84:<\/strong> \uc774\uc804 \uc2dc\uc810\uc758 \uac12\uc774 \ud604\uc7ac \uc2dc\uc810\uc758 \uac12\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc744 \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>I (\ucc28\ubd84) \ubd80\ubd84:<\/strong> \uc815\ud615\uc131\uc744 \ud655\ubcf4\ud558\uae30 \uc704\ud574 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc28\ubd84\ud654\ud558\uc5ec \uc548\uc815\ud654\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>MA (\uc774\ub3d9 \ud3c9\uade0) \ubd80\ubd84:<\/strong> \uc774\uc804 \uc608\uce21 \uc624\ucc28\uac00 \ud604\uc7ac \uc608\uce21\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc744 \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<p>ARIMA \ubaa8\ub378\uc740 <code>ARIMA(p, d, q)<\/code>\uc758 \ud615\ud0dc\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4. \uc5ec\uae30\uc11c <code>p<\/code>\ub294 \uc790\uae30\ud68c\uadc0 \ud56d\uc218, <code>d<\/code>\ub294 \ucc28\ubd84 \ud69f\uc218, <code>q<\/code>\ub294 \uc774\ub3d9 \ud3c9\uade0 \ud56d\uc218\uc785\ub2c8\ub2e4.<\/p>\n<h2>2. \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc218\uc9d1<\/h2>\n<p>\ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \ub370\uc774\ud130 \uc81c\uacf5 API\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 <code>yfinance<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\n<pre><code>pip install yfinance<\/code><\/pre>\n<h3>\ub370\uc774\ud130 \uc218\uc9d1 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\nimport yfinance as yf\nimport pandas as pd\n\n# \ube44\ud2b8\ucf54\uc778 \ub370\uc774\ud130 \uac00\uc838\uc624\uae30\nbtc_data = yf.download('BTC-USD', start='2020-01-01', end='2023-09-30')\nbtc_data['Close'].plot(title='Bitcoin Closing Prices', fontsize=14)\n    <\/code><\/pre>\n<h2>3. \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n<p>ARIMA \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uae30 \uc804, \ub370\uc774\ud130\uc758 \uc548\uc815\uc131\uc744 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud558\uace0, \uc815\uc0c1\uc131 \uac80\uc815\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc815\uc0c1\uc131\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574 ADF(\ud655\uc7a5\ub41c \ub515\ud0a4-\ud480\ub7ec) \uac80\uc815\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>\uc815\uc0c1\uc131 \uac80\uc815 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\nfrom statsmodels.tsa.stattools import adfuller\nimport matplotlib.pyplot as plt\n\n# ADF \uac80\uc815 \ud568\uc218\ndef adf_test(series):\n    result = adfuller(series, autolag='AIC')\n    print('ADF Statistic: %f' % result[0])\n    print('p-value: %f' % result[1])\n    for key, value in result[4].items():\n        print('Critial Values:')\n        print('\\t%s: %.3f' % (key, value))\n\n# \uc885\uac00 \ub370\uc774\ud130\uc5d0 \ub300\ud574 ADF \uac80\uc815 \uc218\ud589\nadf_test(btc_data['Close'])\n    <\/code><\/pre>\n<h2>4. ARIMA \ubaa8\ub378 \ud6c8\ub828<\/h2>\n<p>\ub370\uc774\ud130\uac00 \uc815\uc0c1\uc131\uc744 \uac16\ucd98 \uacbd\uc6b0, ARIMA \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \uc124\uc815\ud558\ub294 \ub370 \uc788\uc5b4 ACF(\uc790\uae30\uc0c1\uad00\ud568\uc218)\uc640 PACF(\ubd80\ubd84\uc790\uae30\uc0c1\uad00\ud568\uc218) \ud50c\ub86f\uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n<h3>ACF \ubc0f PACF \ud50c\ub86f \uc0dd\uc131 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\nfrom statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n\n# ACF \ubc0f PACF \ud50c\ub86f\nplt.figure(figsize=(12, 6))\nplt.subplot(121)\nplot_acf(btc_data['Close'], ax=plt.gca(), lags=30)\nplt.subplot(122)\nplot_pacf(btc_data['Close'], ax=plt.gca(), lags=30)\nplt.show()\n    <\/code><\/pre>\n<h3>ARIMA \ubaa8\ub378 \ud6c8\ub828 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\nfrom statsmodels.tsa.arima.model import ARIMA\n\n# ARIMA \ubaa8\ub378 \uc0dd\uc131 (p, d, q\ub294 \uc801\uc808\ud55c \uac12\uc73c\ub85c \uc124\uc815)\nmodel = ARIMA(btc_data['Close'], order=(5, 1, 0))\nmodel_fit = model.fit()\n\n# \ubaa8\ub378 \uc694\uc57d\nprint(model_fit.summary())\n    <\/code><\/pre>\n<h2>5. \uc608\uce21 \ubc0f \uacb0\uacfc \uc2dc\uac01\ud654<\/h2>\n<p>\ubaa8\ub378\uc744 \ud6c8\ub828\ud55c \ud6c4, \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4. \uc608\uce21 \uacb0\uacfc\uc640 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ube44\uad50\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<h3>\uc608\uce21 \ubc0f \uc2dc\uac01\ud654 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\n# \ubbf8\ub798 30\uc77c \uac00\uaca9 \uc608\uce21\nforecast = model_fit.forecast(steps=30)\nforecast_index = pd.date_range(start='2023-10-01', periods=30)\nforecast_series = pd.Series(forecast, index=forecast_index)\n\n# \uc2e4\uc81c \ub370\uc774\ud130 \uc2dc\uac01\ud654\nplt.figure(figsize=(10, 6))\nplt.plot(btc_data['Close'], label='Actual Prices')\nplt.plot(forecast_series, label='Forecasted Prices', color='red')\nplt.title('Bitcoin Price Forecast')\nplt.xlabel('Date')\nplt.ylabel('Price (USD)')\nplt.legend()\nplt.show()\n    <\/code><\/pre>\n<h2>6. \ubaa8\ub378\uc758 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n<p>\ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 RMSE(Root Mean Squared Error)\uc640 \uac19\uc740 \ud3c9\uac00 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>RMSE \uacc4\uc0b0 \uc608\uc81c \ucf54\ub4dc<\/h3>\n<pre><code>\nfrom sklearn.metrics import mean_squared_error\nimport numpy as np\n\n# RMSE \uacc4\uc0b0\nrmse = np.sqrt(mean_squared_error(btc_data['Close'][-30:], forecast_series))\nprint(f'RMSE: {rmse}')\n    <\/code><\/pre>\n<h2>\uacb0\ub860<\/h2>\n<p>ARIMA \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubaa8\ub378\uc758 \uc131\ub2a5\uc740 \ub370\uc774\ud130\uc758 \ud488\uc9c8, \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \uadf8\ub9ac\uace0 \uc678\ubd80 \uc694\uc778\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\uac00\uc801\uc73c\ub85c \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubc29\ubc95\ub4e4\uacfc \uacb0\ud569\ud558\uc5ec \ub354 \ud5a5\uc0c1\ub41c \uc608\uce21 \uc131\ub2a5\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<div class=\"quote\">\n<strong>\ucc38\uace0:<\/strong> \uc774 \uac15\uc88c\ub294 ARIMA \ubaa8\ub378\uc5d0 \ub300\ud55c \uae30\ucd08 \uac1c\ub150\uc744 \ub2e4\ub8e8\uc5c8\uc73c\uba70, \uc2e4\uc804\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\ub4e4\uc744 \uc811\ubaa9\ud558\uc5ec \ub354\uc6b1 \uc815\uad50\ud55c \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n    <\/div>\n<h2>\uad00\ub828 \uc790\ub8cc \ubc0f \ud559\uc2b5 \uc790\ub8cc<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\">Statsmodels Documentation<\/a><\/li>\n<li><a href=\"https:\/\/pandas.pydata.org\/docs\/\">Pandas Documentation<\/a><\/li>\n<li><a href=\"https:\/\/matplotlib.org\/stable\/index.html\">Matplotlib Documentation<\/a><\/li>\n<li><a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/auto-arima-python\">Auto ARIMA Tutorial<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ube44\ud2b8\ucf54\uc778\uc740 \uae09\uaca9\ud55c \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ubcf4\uc774\uba70 \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc758 \uad00\uc2ec\uc744 \ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc774 \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 ARIMA(AutoRegressive Integrated Moving Average) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f9\ub2c8\ub2e4. 1. ARIMA \ubaa8\ub378 \uac1c\uc694 ARIMA \ubaa8\ub378\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. ARIMA\ub294 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/31401\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uc2dc\uacc4\uc5f4 \uc608\uce21 \ubaa8\ub378 ARIMA  ARIMA \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4 \uc608\uce21.&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-31401","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uc2dc\uacc4\uc5f4 \uc608\uce21 \ubaa8\ub378 ARIMA ARIMA \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4 \uc608\uce21. - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/31401\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uc2dc\uacc4\uc5f4 \uc608\uce21 \ubaa8\ub378 ARIMA ARIMA \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4 \uc608\uce21. - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ube44\ud2b8\ucf54\uc778\uc740 \uae09\uaca9\ud55c \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ubcf4\uc774\uba70 \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc758 \uad00\uc2ec\uc744 \ub04c\uc5c8\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc774 \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 ARIMA(AutoRegressive Integrated Moving Average) \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc2dc\uacc4\uc5f4\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f9\ub2c8\ub2e4. 1. ARIMA \ubaa8\ub378 \uac1c\uc694 ARIMA \ubaa8\ub378\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. 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