{"id":477156,"date":"2023-08-09T09:08:09","date_gmt":"2023-08-09T09:08:09","guid":{"rendered":""},"modified":"2023-09-05T11:14:07","modified_gmt":"2023-09-05T11:14:07","slug":"exponential-smoothing","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/kr\/wiki\/exponential-smoothing\/","title":{"rendered":"\uc9c0\uc218\ud3c9\ud65c"},"content":{"rendered":"<p>\uc9c0\uc218\ud3c9\ud65c\uc740 \uc2dc\uacc4\uc5f4 \ubd84\uc11d \ubc0f \uc608\uce21\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ud1b5\uacc4 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798 \uac00\uce58\ub97c \uc608\uce21\ud558\ub294 \ub370 \ud2b9\ud788 \uc720\uc6a9\ud569\ub2c8\ub2e4. 20\uc138\uae30 \uc911\ubc18\uc5d0 \uac1c\ubc1c\ub41c \uc774 \ubc29\ubc95\uc740 \uacbd\uc81c, \uae08\uc735, \uacf5\uae09\ub9dd \uad00\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcc0\ud654\ud558\ub294 \ucd94\uc138\uc640 \uacc4\uc808\uc131\uc5d0 \uc801\uc751\ud558\ub294 \ub2a5\ub825 \ub355\ubd84\uc5d0 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ud3c9\ud65c\ud654\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<h2>\uc9c0\uc218\ud3c9\ud65c\uc758 \uc720\ub798\uc640 \ucd5c\ucd08 \uc5b8\uae09\uc758 \uc5ed\uc0ac<\/h2>\n<p>\uc9c0\uc218\ud3c9\ud65c\uc758 \uac1c\ub150\uc740 1956\ub144 Robert Goodell Brown\uc5d0 \uc758\ud574 \ucc98\uc74c \uc18c\uac1c\ub418\uc5c8\uc73c\uba70, \uadf8\ub294 Journal of the Operations Research Society of America\uc5d0 &quot;\uc218\uc694 \uc608\uce21\uc744 \uc704\ud55c \uc9c0\uc218\ud3c9\ud65c&quot;\uc774\ub77c\ub294 \uc81c\ubaa9\uc758 \ub17c\ubb38\uc744 \ubc1c\ud45c\ud588\uc2b5\ub2c8\ub2e4. \ube0c\ub77c\uc6b4\uc758 \uc791\uc5c5\uc740 \uc774 \uac15\ub825\ud55c \uc608\uce21 \uae30\uc220\uc758 \ud1a0\ub300\ub97c \ub9c8\ub828\ud588\uc73c\uba70 \uc774\ud6c4 \uc218\ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uc2e4\ubb34\uc790\ub4e4\uc5d0 \uc758\ud574 \ud655\uc7a5\ub418\uace0 \uac1c\uc120\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uc9c0\uc218\ud3c9\ud65c\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \uc815\ubcf4<\/h2>\n<p>\uc9c0\uc218\ud3c9\ud65c\uc740 \uacfc\uac70 \uad00\uce21\uce58\uc5d0 \uae30\ud558\uae09\uc218\uc801\uc73c\ub85c \uac10\uc18c\ud558\ub294 \uac00\uc911\uce58\ub97c \ud560\ub2f9\ud558\ub294 \uc6d0\ub9ac\uc5d0 \ub530\ub77c \uc791\ub3d9\ud558\uba70, \ucd5c\uadfc \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub294 \uc774\uc804 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ubcf4\ub2e4 \ub354 \ub192\uc740 \uac00\uc911\uce58\ub97c \ubc1b\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uac00\uc911\uce58\uac00 \uac10\uc18c\ud558\ub294 \uc18d\ub3c4\ub97c \uc81c\uc5b4\ud558\ub294 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218(\uc54c\ud30c)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc2dc\uac04 t+1\uc5d0\uc11c\uc758 \uc608\uce21\uac12(F(t+1)\ub85c \ud45c\uc2dc\ub428)\uc740 \ub2e4\uc74c \uacf5\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/p>\n<p>F(t+1) = \u03b1 * D(t) + (1 \u2013 \u03b1) * F(t)<\/p>\n<p>\uc5b4\ub514:<\/p>\n<ul>\n<li>F(t+1)\uc740 t+1 \uc2dc\uc810\uc758 \uc608\uce21\uac12\uc785\ub2c8\ub2e4.<\/li>\n<li>D(t)\ub294 \uc2dc\uac04 t\uc5d0\uc11c \uad00\ucc30\ub41c \uc2e4\uc81c \uac12\uc785\ub2c8\ub2e4.<\/li>\n<li>F(t)\ub294 \uc2dc\uac04 t\uc5d0\uc11c\uc758 \uc608\uce21\uac12\uc785\ub2c8\ub2e4.<\/li>\n<li>\u03b1\ub294 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218\ub85c, \uc885\uc885 0\uacfc 1 \uc0ac\uc774\ub85c \uc124\uc815\ub429\ub2c8\ub2e4.<\/li>\n<\/ul>\n<p>\uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ub418\uba74 \uc608\uce21\uc774 \uc5c5\ub370\uc774\ud2b8\ub418\uc5b4 \ucd5c\uadfc \uad00\uce21\uce58\uc5d0 \ub354 \ud070 \uc911\uc694\uc131\uc744 \ubd80\uc5ec\ud558\ub294 \ub3d9\uc2dc\uc5d0 \uc624\ub798\ub41c \ub370\uc774\ud130\uc758 \uc601\ud5a5\uc744 \uc810\ucc28\uc801\uc73c\ub85c \uc904\uc785\ub2c8\ub2e4. \u03b1 \uac12\uc740 \ubaa8\ub378\uc774 \uae30\ubcf8 \ub370\uc774\ud130\uc758 \ubcc0\ud654\uc5d0 \uc5bc\ub9c8\ub098 \ubc18\uc751\ud558\ub294\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4.<\/p>\n<h2>\uc9c0\uc218\ud3c9\ud65c\uc758 \ub0b4\ubd80 \uad6c\uc870: \uc9c0\uc218\ud3c9\ud65c\uc758 \uc791\ub3d9 \ubc29\uc2dd<\/h2>\n<p>\uc9c0\uc218\ud3c9\ud65c\uc740 \uc0ac\uc6a9\ub418\ub294 \ud3c9\ud65c \ub9e4\uac1c\ubcc0\uc218\uc758 \uc218\uc5d0 \ub530\ub77c \ub2e8\uc21c \uc9c0\uc218 \ud3c9\ud65c, \uc774\uc911 \uc9c0\uc218 \ud3c9\ud65c, \uc0bc\uc911 \uc9c0\uc218 \ud3c9\ud65c(\ud640\ud2b8-\uc708\ud130\uc2a4 \ubc29\ubc95)\uc758 \uc138 \uac00\uc9c0 \uc8fc\uc694 \uc720\ud615\uc73c\ub85c \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc720\ud615\uc758 \uc9c0\uc218\ud3c9\ud65c\uc740 \ud2b9\uc815 \ubaa9\uc801\uc744 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<ol>\n<li>\n<p>\ub2e8\uc21c \uc9c0\uc218\ud3c9\ud65c:<\/p>\n<ul>\n<li>\ud558\ub098\uc758 \ud3c9\ud65c \ub9e4\uac1c\ubcc0\uc218(\u03b1)\ub9cc \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li>\ub69c\ub837\ud55c \ucd94\uc138\ub098 \uacc4\uc808\uc131\uc774 \uc5c6\ub294 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/li>\n<li>\uae30\ubcf8 \ud504\ub85c\uc138\uc2a4\uac00 \ub4dc\ub9ac\ud504\ud2b8\uac00 \uc788\ub294 \ub79c\ub364 \uc6cc\ud06c\ub77c\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\uc774\uc911 \uc9c0\uc218\ud3c9\ud65c(\ud640\ud2b8\uc758 \ubc29\ubc95):<\/p>\n<ul>\n<li>\ub450 \uac1c\uc758 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218(\u03b1 \ubc0f \u03b2)\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc120\ud615 \ucd94\uc138\uac00 \uc788\uc9c0\ub9cc \uacc4\uc808\uc131\uc774 \uc5c6\ub294 \ub370\uc774\ud130\uc5d0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/li>\n<li>\uae30\ubcf8 \ud504\ub85c\uc138\uc2a4\uac00 \uc120\ud615 \ucd94\uc138\ub97c \ub530\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\uc0bc\uc911 \uc9c0\uc218 \ud3c9\ud65c(\ud640\ud2b8-\uc708\ud130\uc2a4 \ubc29\ubc95):<\/p>\n<ul>\n<li>\uc138 \uac00\uc9c0 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218(\u03b1, \u03b2, \u03b3)\ub97c \ud1b5\ud569\ud569\ub2c8\ub2e4.<\/li>\n<li>\ucd94\uc138\uc640 \uacc4\uc808\uc131\uc774 \ubaa8\ub450 \uc788\ub294 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/li>\n<li>\uae30\ubcf8 \ud504\ub85c\uc138\uc2a4\uac00 \uc120\ud615 \ucd94\uc138\ub97c \uac00\uc9c0\uba70 \uacc4\uc808\uc801 \ud328\ud134\uc744 \ub530\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\uc9c0\uc218\ud3c9\ud65c\uc758 \uc8fc\uc694 \ud2b9\uc9d5 \ubd84\uc11d<\/h2>\n<p>\uc9c0\uc218 \ud3c9\ud65c\uc740 \uc2dc\uacc4\uc5f4 \uc608\uce21\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \uba87 \uac00\uc9c0 \uc8fc\uc694 \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n<ol>\n<li>\n<p>\ub2e8\uc21c\uc131: \uc774 \ubc29\ubc95\uc740 \uad6c\ud604 \ubc0f \ud574\uc11d\uc774 \uc26c\uc6b0\ubbc0\ub85c \ube44\uc804\ubb38\uac00\ub97c \ud3ec\ud568\ud55c \uad11\ubc94\uc704\ud55c \uc0ac\uc6a9\uc790\uac00 \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc720\uc5f0\uc131: \ub2e4\uc591\ud55c \ubcc0\ud615(\ub2e8\uc21c, \uc774\uc911 \ubc0f \uc0bc\uc911)\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\ubbc0\ub85c \uc9c0\uc218\ud3c9\ud65c\uc740 \ub2e4\uc591\ud55c \uc720\ud615\uc758 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc801\uc751\uc131: \uc774 \ubc29\ubc95\uc740 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uac00 \uc81c\uacf5\ub420 \ub54c \uc608\uce21 \ubaa8\ub378\uc744 \uc790\ub3d9\uc73c\ub85c \uc870\uc815\ud558\uc5ec \uae30\ubcf8 \ud328\ud134\uc758 \ubcc0\ud654\uc5d0 \ub300\uc751\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uac00\uc911 \ud3c9\uade0\ud654: \uc9c0\uc218 \ud3c9\ud65c\uc740 \ucd5c\uadfc \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc5d0 \ub354 \uc911\uc810\uc744 \ub450\uc5b4 \uc804\ubc18\uc801\uc778 \ucd94\uc138\ub97c \uace0\ub824\ud558\uba74\uc11c \ub2e8\uae30\uc801\uc778 \ubcc0\ub3d9\uc744 \ud3ec\ucc29\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uacc4\uc0b0 \ud6a8\uc728\uc131: \uc9c0\uc218\ud3c9\ud65c\uacfc \uad00\ub828\ub41c \uacc4\uc0b0\uc740 \uc0c1\ub300\uc801\uc73c\ub85c \uac04\ub2e8\ud558\ubbc0\ub85c \uc2e4\uc2dc\uac04 \uc608\uce21\uc5d0 \uacc4\uc0b0\uc801\uc73c\ub85c \ud6a8\uc728\uc801\uc785\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ol>\n<h2>\uc9c0\uc218\ud3c9\ud65c\uc758 \uc720\ud615<\/h2>\n<table>\n<thead>\n<tr>\n<th>\uc720\ud615<\/th>\n<th>\uc124\uba85<\/th>\n<th>\ub370\uc774\ud130\uc5d0 \uc801\ud569<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\ub2e8\uc21c \uc9c0\uc218\ud3c9\ud65c<\/td>\n<td>\ub2e8\uc77c \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/td>\n<td>\ucd94\uc138\ub098 \uacc4\uc808\uc131\uc774 \uc5c6\uc2b5\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uc774\uc911 \uc9c0\uc218\ud3c9\ud65c<\/td>\n<td>\ub450 \uac1c\uc758 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4.<\/td>\n<td>\uc120\ud615 \ucd94\uc138\uc774\uba70 \uacc4\uc808\uc131\uc774 \uc5c6\uc2b5\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uc0bc\uc911 \uc9c0\uc218\ud3c9\ud65c<\/td>\n<td>\uc138 \uac00\uc9c0 \ud3c9\ud65c\ud654 \ub9e4\uac1c\ubcc0\uc218\ub97c \ud1b5\ud569\ud569\ub2c8\ub2e4.<\/td>\n<td>\ub3d9\ud5a5\uacfc \uacc4\uc808\uc131.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\uc9c0\uc218\ud3c9\ud65c\ubc95\uc758 \ud65c\uc6a9\ubc29\ubc95\uacfc \ud65c\uc6a9\uc5d0 \ub530\ub978 \ubb38\uc81c\uc810 \ubc0f \ud574\uacb0\ubc29\uc548<\/h2>\n<p>\uc9c0\uc218 \ud3c9\ud65c\uc740 \ub2e4\uc74c\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ub3c4\uba54\uc778\uc5d0\uc11c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/p>\n<ol>\n<li>\n<p>\uc218\uc694 \uc608\uce21: \uae30\uc5c5\uc740 \uc9c0\uc218\ud3c9\ud65c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc81c\ud488\uc774\ub098 \uc11c\ube44\uc2a4\uc5d0 \ub300\ud55c \ubbf8\ub798 \uc218\uc694\ub97c \uc608\uce21\ud558\uace0 \uc7ac\uace0 \uad00\ub9ac \ubc0f \uacf5\uae09\ub9dd \ucd5c\uc801\ud654\ub97c \ub3d5\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc7ac\ubb34 \ubd84\uc11d: \uc9c0\uc218 \ud3c9\ud65c\ubc95\uc740 \ubd84\uc11d\uac00\uac00 \ud310\ub9e4, \uc218\uc775, \ud604\uae08 \ud750\ub984\uacfc \uac19\uc740 \uc7ac\ubb34 \uc9c0\ud45c\ub97c \uc608\uce21\ud558\uace0 \uc608\uc0b0 \ucc45\uc815 \ubc0f \uc7ac\ubb34 \uacc4\ud68d\uc744 \uc138\uc6b0\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc790\uc6d0 \uacc4\ud68d: \uc870\uc9c1\uc740 \uc9c0\uc218\ud3c9\ud65c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc778\ub825 \uc77c\uc815 \ubc0f \uc0dd\uc0b0 \ub2a5\ub825\uacfc \uac19\uc740 \uc790\uc6d0 \ud560\ub2f9\uc744 \uacc4\ud68d\ud569\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ol>\n<p>\uc9c0\uc218 \ud3c9\ud65c\ud654\uc758 \uacfc\uc81c:<\/p>\n<ol>\n<li>\n<p>\ub9e4\uac1c\ubcc0\uc218\uc5d0 \ub300\ud55c \ubbfc\uac10\ub3c4: \uc9c0\uc218\ud3c9\ud65c \ubaa8\ub378\uc758 \uc131\ub2a5\uc740 \ud3c9\ud65c \ub9e4\uac1c\ubcc0\uc218 \uc120\ud0dd\uc5d0 \ubbfc\uac10\ud558\uc5ec \ucc28\uc120\ucc45 \uc608\uce21\uc73c\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc774\uc0c1\uac12 \ucc98\ub9ac: \uc9c0\uc218\ud3c9\ud65c\uc740 \uc2dc\uacc4\uc5f4\uc758 \uc774\uc0c1\uac12\uc774\ub098 \uae09\uaca9\ud55c \ubcc0\ud654\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uc5b4\ub824\uc6c0\uc744 \uacaa\uc744 \uc218 \uc788\uc73c\uba70 \uc7a0\uc7ac\uc801\uc73c\ub85c \uc608\uce21\uc758 \uc815\ud655\uc131\uc5d0 \uc601\ud5a5\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ol>\n<p>\uc9c0\uc218\ud3c9\ud65c\uc744 \uac1c\uc120\ud558\ub294 \uc194\ub8e8\uc158:<\/p>\n<ol>\n<li>\n<p>\ub9e4\uac1c\ubcc0\uc218 \ucd5c\uc801\ud654: \uad50\ucc28 \uac80\uc99d \ubc0f \uadf8\ub9ac\ub4dc \uac80\uc0c9\uc744 \ud1b5\ud55c \uc2e0\uc911\ud55c \ub9e4\uac1c\ubcc0\uc218 \uc870\uc815\uc740 \ubaa8\ub378 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<li>\n<p>\uc774\uc0c1\uce58 \uac10\uc9c0: \uc774\uc0c1\uce58 \uac10\uc9c0 \ubc0f \ub370\uc774\ud130 \ubcc0\ud658\uacfc \uac19\uc740 \uc804\ucc98\ub9ac \uae30\uc220\uc740 \uc774\uc0c1\uce58\uc758 \uc601\ud5a5\uc744 \uc644\ud654\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/li>\n<\/ol>\n<h2>\uc8fc\uc694 \ud2b9\uc9d5 \ubc0f \uae30\ud0c0 \uc720\uc0ac \uc6a9\uc5b4\uc640\uc758 \ube44\uad50<\/h2>\n<table>\n<thead>\n<tr>\n<th>\uc6a9\uc5b4<\/th>\n<th>\uc124\uba85<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\uc9c0\uc218\ud3c9\ud65c<\/td>\n<td>\uacfc\uac70 \uad00\uce21\uce58\uc758 \uac00\uc911 \ud3c9\uade0\uc744 \uc774\uc6a9\ud55c \uc2dc\uacc4\uc5f4 \uc608\uce21 \uae30\ubc95.<\/td>\n<\/tr>\n<tr>\n<td>\uc774\ub3d9 \ud3c9\uade0<\/td>\n<td>\uace0\uc815\ub41c \ub370\uc774\ud130 \ucc3d\uc5d0 \ub300\ud55c \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ub610 \ub2e4\ub978 \uc2dc\uacc4\uc5f4 \ud3c9\ud65c\ud654 \uae30\uc220\uc785\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uacc4\uc808 \ubd84\ud574<\/td>\n<td>\uc2dc\uacc4\uc5f4\uc744 \ucd94\uc138, \uacc4\uc808\uc131, \uc794\ucc28 \uc131\ubd84\uc73c\ub85c \ubd84\ub9ac\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uc790\uae30\ud68c\uadc0 \ud1b5\ud569 \uc774\ub3d9 \ud3c9\uade0(ARIMA)<\/td>\n<td>\ub370\uc774\ud130 \ucc28\uc774, \uc790\ub3d9 \ud68c\uadc0 \ubc0f \uc774\ub3d9 \ud3c9\uade0\uc744 \ubaa8\ub378\ub9c1\ud558\ub294 \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \uc2dc\uacc4\uc5f4 \uc608\uce21 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\uc9c0\uc218\ud3c9\ud65c\ud654\uc5d0 \uad00\ud55c \ubbf8\ub798\uc758 \uad00\uc810\uacfc \uae30\uc220<\/h2>\n<p>\uc9c0\uc218 \ud3c9\ud65c\uc740 \ub2e8\uc21c\uc131\uacfc \ud6a8\uc728\uc131\uc73c\ub85c \uc778\ud574 \uc55e\uc73c\ub85c\ub3c4 \uacc4\uc18d \uad00\ub828\uc131\uc774 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uae30\uacc4 \ud559\uc2b5\uacfc \uc778\uacf5 \uc9c0\ub2a5\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ubcf5\uc7a1\ud55c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ub354 \uc815\ud655\ud558\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \ub354\uc6b1 \uc815\uad50\ud55c \uc608\uce21 \uae30\uc220\uc774 \ub3c4\uc785\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\ud504\ub85d\uc2dc \uc11c\ubc84\ub97c \uc9c0\uc218 \ud3c9\ud65c\ud654\uc640 \uc0ac\uc6a9\ud558\uac70\ub098 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95<\/h2>\n<p>\ud504\ub85d\uc2dc \uc11c\ubc84\ub294 \uc778\ud130\ub137\uc744 \uc0ac\uc6a9\ud558\ub294 \ub3d9\uc548 \uc775\uba85\uc131\uacfc \uac1c\uc778 \uc815\ubcf4 \ubcf4\ud638\ub97c \ubcf4\uc7a5\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \ub54c, \ud2b9\ud788 \uc608\uce21\uc744 \uc775\uba85\uc73c\ub85c \uc218\ud589\ud574\uc57c \ud558\ub294 \uc2dc\ub098\ub9ac\uc624\uc5d0\uc11c\ub294 \ud504\ub85d\uc2dc \uc11c\ubc84\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc6a9\uc790\uc758 \uc2e0\uc6d0\uacfc \uc704\uce58\ub97c \ub9c8\uc2a4\ud0b9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ubbfc\uac10\ud55c \ub370\uc774\ud130\ub098 \ub3c5\uc810 \uc815\ubcf4\uac00 \uad00\ub828\ub41c \uacbd\uc6b0\uc5d0 \ud2b9\ud788 \uad00\ub828\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uad00\ub828\ub41c \ub9c1\ud06c\ub4e4<\/h2>\n<p>\uc9c0\uc218 \ud3c9\ud65c\ud654\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uc744 \ubcf4\ub824\uba74 \ub2e4\uc74c \ub9ac\uc18c\uc2a4\ub97c \ud0d0\uc0c9\ud558\uc138\uc694.<\/p>\n<ol>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Exponential_smoothing\" target=\"_new\" rel=\"noopener nofollow\">Wikipedia \u2013 \uc9c0\uc218\ud3c9\ud65c<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/time-series-forecasting-with-exponential-smoothing-in-python-30d037a0d48d\" target=\"_new\" rel=\"noopener nofollow\">\ub370\uc774\ud130 \uacfc\ud559\uc744 \ud5a5\ud558\uc5ec - Python\uc758 \uc9c0\uc218 \ud3c9\ud65c\ud654\ub97c \uc0ac\uc6a9\ud55c \uc2dc\uacc4\uc5f4 \uc608\uce21<\/a><\/li>\n<li><a href=\"https:\/\/otexts.com\/fpp2\/expsmooth.html\" target=\"_new\" rel=\"noopener nofollow\">\uc608\uce21: \uc6d0\ub9ac \ubc0f \uc2e4\uc2b5 \u2013 \uc9c0\uc218\ud3c9\ud65c<\/a><\/li>\n<\/ol>\n<p>\uacb0\ub860\uc801\uc73c\ub85c, \uc9c0\uc218\ud3c9\ud65c\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \uc2dc\uacc4\uc5f4 \uc608\uce21\uc744 \uc704\ud55c \ub2e4\uc7ac\ub2e4\ub2a5\ud558\uace0 \ud6a8\uacfc\uc801\uc778 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ubcc0\ud654\ud558\ub294 \ud328\ud134\uc5d0 \uc801\uc751\ud558\ub294 \ub2a5\ub825\uacfc \uad6c\ud604\uc758 \ub2e8\uc21c\uc131\uc740 \uae30\uc5c5\uacfc \uc5f0\uad6c\uc790\ub4e4 \ubaa8\ub450\uc5d0\uac8c \uadc0\uc911\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uae30\uc220\uc774 \uacc4\uc18d \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c \uc9c0\uc218\ud3c9\ud65c\uc740 \ub354\uc6b1 \ubc1c\uc804\ub41c \uc608\uce21 \uae30\ubc95\uacfc \uacf5\uc874\ud558\uc5ec \uc55e\uc73c\ub85c \ub2e4\uc591\ud55c \uc608\uce21 \uc694\uad6c\ub97c \ucda9\uc871\ud560 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.<\/p>","protected":false},"featured_media":468360,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477156","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Exponential Smoothing: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is exponential smoothing?","answer":"<p>Exponential smoothing is a statistical technique used in time series analysis and forecasting. It assigns decreasing weights to past data points, with recent observations receiving higher importance. This method adapts to changing trends and seasonality, making it valuable for predicting future values based on historical data.<\/p>"},{"question":"Who introduced exponential smoothing?","answer":"<p>Exponential smoothing was first introduced by Robert Goodell Brown in 1956 through his paper titled \"Exponential Smoothing for Predicting Demand.\"<\/p>"},{"question":"How does exponential smoothing work?","answer":"<p>Exponential smoothing uses a smoothing parameter (alpha) to calculate forecasted values. The formula for forecasting at time t+1 is F(t+1) = \u03b1 * D(t) + (1 - \u03b1) * F(t), where F(t+1) is the forecasted value at time t+1, D(t) is the actual value at time t, and F(t) is the forecasted value at time t.<\/p>"},{"question":"What are the main types of exponential smoothing?","answer":"<p>There are three main types of exponential smoothing:<\/p><ol><li>Simple Exponential Smoothing: Uses one smoothing parameter and is suitable for data without trends or seasonality.<\/li><li>Double Exponential Smoothing: Utilizes two smoothing parameters and is effective for data with a linear trend but no seasonality.<\/li><li>Triple Exponential Smoothing: Incorporates three smoothing parameters and is ideal for data with trends and seasonality.<\/li><\/ol>"},{"question":"Where is exponential smoothing used?","answer":"<p>Exponential smoothing finds applications in various fields, including demand forecasting, financial analysis, and resource planning.<\/p>"},{"question":"What are the challenges with using exponential smoothing?","answer":"<p>Exponential smoothing models can be sensitive to the choice of smoothing parameters and may struggle to handle outliers or sudden changes in the time series data.<\/p>"},{"question":"How can the performance of exponential smoothing be improved?","answer":"<p>The performance of exponential smoothing can be improved through careful parameter optimization and preprocessing techniques like outlier detection and data transformation.<\/p>"},{"question":"Is exponential smoothing a future-proof technique?","answer":"<p>While exponential smoothing is likely to remain relevant due to its simplicity and effectiveness, advancements in machine learning and AI may introduce more sophisticated forecasting techniques in the future.<\/p>"},{"question":"How are proxy servers associated with exponential smoothing?","answer":"<p>Proxy servers can be used to mask the user's identity and location, making them useful when dealing with time series data in scenarios where anonymity is essential.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477156","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477156\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media\/468360"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media?parent=477156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}