{"id":5570,"date":"2022-05-26T08:53:34","date_gmt":"2022-05-25T23:53:34","guid":{"rendered":"https:\/\/gri.jp\/media\/?p=5570"},"modified":"2023-06-05T18:57:24","modified_gmt":"2023-06-05T09:57:24","slug":"%e6%99%82%e7%b3%bb%e5%88%97%e3%83%87%e3%83%bc%e3%82%bf%e3%81%8b%e3%82%89%e5%a4%a7%e9%87%8f%e3%81%ae%e7%89%b9%e5%be%b4%e9%87%8f%e3%82%92%e7%94%9f%e6%88%90%e3%81%99%e3%82%8b%e3%83%91%e3%83%83%e3%82%b1","status":"publish","type":"post","link":"https:\/\/gri.jp\/media\/entry\/5570","title":{"rendered":"\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304b\u3089\u5927\u91cf\u306e\u7279\u5fb4\u91cf\u3092\u751f\u6210\u3059\u308b\u30d1\u30c3\u30b1\u30fc\u30b8\u300ctsfresh\u300d\u306e\u4f7f\u3044\u65b9"},"content":{"rendered":"<p>\u3053\u3093\u306b\u3061\u306f\uff01\u7a81\u7136\u3067\u3059\u304c\u3001\u7686\u3055\u3093\u306f\u4e0b\u306e\u3088\u3046\u306a\u4e8c\u7a2e\u985e\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u5224\u5225\u3067\u304d\u308b\u3088\u3046\u306a\u7279\u5fb4\u91cf\u3092\u629c\u304d\u51fa\u3057\u305f\u3044\u3068\u304d\u306b\u4f55\u3092\u8003\u3048\u307e\u3059\u304b\uff1f\u305d\u3057\u3066\u3069\u3046\u3084\u3063\u3066\u7279\u5fb4\u91cf\u3092\u62bd\u51fa\u3057\u307e\u3059\u304b\uff1f<img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-6080 size-full\" src=\"https:\/\/gri.jp\/media\/wp\/wp-content\/uploads\/2022\/03\/267-e1648480131111.png\" alt=\"\" width=\"365\" height=\"252\" \/><\/p>\n<img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-6081 size-full\" style=\"letter-spacing: 0.8px;\" src=\"https:\/\/gri.jp\/media\/wp\/wp-content\/uploads\/2022\/03\/279-e1648481362402.png\" alt=\"\" width=\"361\" height=\"250\" \/>\n<p>\u79c1\u306f\u30d1\u30c3\u3068\u898b\u3066\u6b21\u306e\u624b\u6cd5\u3092\u4f7f\u3048\u3070\u7279\u6027\u304c\u53d6\u308a\u51fa\u305b\u308b\u3068\u601d\u3044\u307e\u3057\u305f\u3002<\/p>\n<ul>\n<li>\u30d4\u30fc\u30af\u306e\u6570 \u2192 k\u8fd1\u508d\u6cd5<\/li>\n<li>\u30ce\u30a4\u30ba\u306e\u5927\u304d\u3055 \u2192 \u5206\u6563\u7d71\u8a08\u91cf<\/li>\n<li>\u6642\u7cfb\u5217\u65b9\u5411\u3067\u5468\u671f\u6210\u5206\u306e\u5927\u304d\u3055 \u2192 Wavelet\u5909\u63db<\/li>\n<\/ul>\n<p>\u3057\u304b\u3057\u3001\u5f53\u7136\u3053\u308c\u3060\u3051\u3067\u306f\u5341\u5206\u306a\u6570\u306e\u7279\u6027\u3092\u7db2\u7f85\u3067\u304d\u3066\u3044\u306a\u3044\u3067\u3057\u3087\u3046\u3057\u3001\u9069\u5207\u306a\u7279\u6027\u3092\u629c\u304d\u51fa\u3059\u305f\u3081\u306b\u30d1\u30e9\u30e1\u30fc\u30bf\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\uff08\u4f8b\u3048\u3070\u3001Wavelet\u5909\u63db\u3067\u3042\u308c\u3070\u9069\u5207\u306a\u57fa\u5e95\u95a2\u6570\u3092\u9078\u3076\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\uff09\u3002<\/p>\n<p>\u3053\u306e\u3088\u3046\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306f\u8abf\u3079\u308b\u3053\u3068\u304c\u7121\u9650\u306b\u3042\u308a\u3001\u3069\u306e\u7279\u5fb4\u91cf\u3092\u7b97\u51fa\u3059\u308b\u304b\u3092\u8003\u3048\u3066\u3044\u308b\u3060\u3051\u3067\u65e5\u304c\u66ae\u308c\u3066\u3057\u307e\u3044\u307e\u3059\u3002\u307e\u305f\u3001\u629c\u304d\u51fa\u3059\u7279\u5fb4\u91cf\u304c\u6c7a\u307e\u3063\u305f\u3068\u3057\u3066\u3082\u30e2\u30ce\u306b\u3088\u3063\u3066\u306f\u8a08\u7b97\u304c\u8907\u96d1\u3067\u5b9f\u88c5\u306b\u6642\u9593\u304c\u304b\u304b\u3063\u3066\u3057\u307e\u3046\u5834\u5408\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u305d\u3093\u306a\u3068\u304d\u306b\u6709\u529b\u306a\u9078\u629e\u80a2\u306b\u306a\u308b\u306e\u304c Python\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3067\u3042\u308b tsfresh\u3067\u3059\u3002\u3053\u308c\u306f\u305f\u3063\u305f\u6570\u884c\u306e\u30b3\u30fc\u30c9\u3067\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304b\u3089\u5927\u91cf\u306e\u7279\u5fb4\u91cf\u8a08\u7b97\u3092\u884c\u3063\u3066\u304f\u308c\u308b\u30d1\u30c3\u30b1\u30fc\u30b8\u3067\u3059\u3002<\/p>\n<ul>\n<li><a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/\" target=\"_blank\" rel=\"noopener\">\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/blue-yonder\/tsfresh\" target=\"_blank\" rel=\"noopener\">GitHub<\/a><\/li>\n<\/ul>\n<p>\u524d\u7f6e\u304d\u304c\u9577\u304f\u306a\u308a\u307e\u3057\u305f\u304c\u3001\u4eca\u56de\u306f\u3053\u306e tsfresh\u306e\u4f7f\u3044\u65b9\u306b\u3064\u3044\u3066\u66f8\u3044\u3066\u3044\u304d\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n<div id=\"rtoc-mokuji-wrapper\" class=\"rtoc-mokuji-content frame1 animation-fade rtoc_open noto-sans\" data-id=\"5570\">\n<div id=\"rtoc-mokuji-title\" class=\"rtoc_left\"><button class=\"rtoc_open_close rtoc_open\"><\/button><span>\u76ee\u6b21<\/span><\/div>\n<ul class=\"rtoc-mokuji mokuji_none level-1\">\n<li class=\"rtoc-item\"><a href=\"#rtoc-1\">tsfresh\u304c\u3067\u304d\u308b\u3053\u3068\u30fb\u3067\u304d\u306a\u3044\u3053\u3068\u4e00\u89a7<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-2\">\u74b0\u5883\u69cb\u7bc9<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-3\">tsfresh\u306b\u5165\u308c\u308b\u30c7\u30fc\u30bf\u306e\u6e96\u5099<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-4\">\u7279\u5fb4\u91cf\u751f\u6210<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-5\">\u7279\u5fb4\u91cf\u9078\u629e<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-6\">\u751f\u6210\u3059\u308b\u7279\u5fb4\u91cf\u3092\u9078\u5225<\/a><\/li>\n<li class=\"rtoc-item\"><a href=\"#rtoc-7\">\u79fb\u52d5\u7a93\u3054\u3068\u306e\u7279\u5fb4\u91cf\u751f\u6210<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"rtoc-1\" >tsfresh\u304c\u3067\u304d\u308b\u3053\u3068\u30fb\u3067\u304d\u306a\u3044\u3053\u3068\u4e00\u89a7<\/h2>\n<p>tsfresh\u304c\u3067\u304d\u308b\u3053\u3068\u306f\u6b21\u306e\u901a\u308a\u3067\u3059\uff08\u5168\u3066\u3067\u306f\u306a\u304f\u3001\u4e00\u90e8\u5206\u3092\u629c\u7c8b\u3057\u3066\u3044\u307e\u3059\uff09<\/p>\n<ul>\n<li>\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304b\u3089\u30e6\u30cb\u30fc\u30af\u306a\u756a\u53f7\u3054\u3068\u306b\u7d04800\u500b\u7a0b\u5ea6\u306e\u7279\u5fb4\u91cf\u3092\u751f\u6210<\/li>\n<li>\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u304c\u8907\u6570\u7a2e\u985e\u3042\u308c\u3070\u305d\u306e\u6570\u5206\u306e\u7279\u5fb4\u91cf\u3092\u751f\u6210<\/li>\n<li>\u7121\u9650\u5927\u3084\u6b20\u640d\u5024\u3092\u4e00\u884c\u3067\u51e6\u7406<\/li>\n<li>\u30bf\u30fc\u30b2\u30c3\u30c8\u3068\u306e\u76f8\u95a2\u6027\u3092\u6709\u610f\u5dee\u691c\u5b9a\u306b\u3088\u3063\u3066\u5224\u5225\u3057\u3001\u76f8\u95a2\u6027\u304c\u306a\u3044\u7279\u5fb4\u91cf\u3092\u524a\u9664<\/li>\n<li>\u751f\u6210\u3059\u308b\u7279\u5fb4\u91cf\u3092\u9078\u5225<\/li>\n<li>\u79fb\u52d5\u7a93\u3054\u3068\u306b\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u3092\u632f\u308a\u76f4\u3059<\/li>\n<\/ul>\n<p>\u9006\u306b tsfresh\u304c\u3067\u304d\u306a\u3044\u3053\u3068\u306f\u6b21\u306e\u901a\u308a\u3067\u3059<\/p>\n<ul>\n<li>\u69d8\u3005\u306a\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u7279\u5fb4\u91cf\u5909\u63db\uff08\u5165\u308c\u308b\u30c7\u30fc\u30bf\u306f\u7279\u5b9a\u306e\u578b\u3067\u306a\u3044\u3068\u3044\u3051\u306a\u3044\uff09<\/li>\n<li>\u751f\u6210\u3055\u308c\u305f\u7279\u5fb4\u91cf\u306e\u610f\u5473\u3092\u7406\u89e3\uff08\u6570\u5b66\u306e\u77e5\u8b58\u304c\u5fc5\u8981\uff09<\/li>\n<li>\u751f\u6210\u3055\u308c\u305f\u7279\u5fb4\u91cf\u540c\u58eb\u306e\u76f8\u95a2\u6027\u3092\u8abf\u67fb\uff08\u591a\u91cd\u5171\u7dda\u6027\u306e\u8a98\u767a\uff09<\/li>\n<\/ul>\n<p>\u305d\u308c\u3067\u306f\u8aac\u660e\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<h2 id=\"rtoc-2\" >\u74b0\u5883\u69cb\u7bc9<\/h2>\n<p>tsfresh\u30d1\u30c3\u30b1\u30fc\u30b8\u306f<code>pip install<\/code>\u3067\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3067\u304d\u307e\u3059<\/p>\n<pre class=\"language-markup\"><code>pip install tsfresh<\/code><\/pre>\n<h2 id=\"rtoc-3\" >tsfresh\u306b\u5165\u308c\u308b\u30c7\u30fc\u30bf\u306e\u6e96\u5099<\/h2>\n<p>tsfresh\u304c\u5909\u63db\u53ef\u80fd\u306a\u30c7\u30fc\u30bf\u306e\u578b\u306f pandas.DataFrame\u304b Dict\u578b\u3001\u307e\u305f\u306f\u3053\u308c\u3089\u306e\u30c7\u30fc\u30bf\u304c\u683c\u7d0d\u3067\u304d\u306a\u3044\u3088\u3046\u306a\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u306e\u5834\u5408\u306f dask.dataframe\u307e\u305f\u306f pyspark\u306e\u3044\u305a\u308c\u304b\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u3001pandas.DataFrame\u306b\u7d5e\u3063\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n<p>tsfresh\u3067\u7279\u5fb4\u91cf\u751f\u6210\u3092\u3059\u308b\u305f\u3081\u306b\u5fc5\u305a\u5fc5\u8981\u306a\u30ab\u30e9\u30e0\u306f\u6b21\u306e\u4e09\u3064\u3067\u3059\u3002\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u306fid\u00d7time \u3067\u3042\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\u4e00\u3064\u4e00\u3064\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u8b58\u5225\u3059\u308b\u30e6\u30cb\u30fc\u30af\u306a\u756a\u53f7\uff1aid<\/li>\n<li>\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u6642\u9593\uff08\u904e\u53bb\u3068\u672a\u6765\u3092\u8868\u73fe\u3059\u308b\u8ef8\uff09\uff1atime<\/li>\n<li>\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u5024\uff08\u8907\u6570\u5217\u3042\u3063\u3066\u3082\u53ef\uff09\uff1avalue<\/li>\n<\/ul>\n<p>pandas.Dataframe\u98a8\u306b\u66f8\u304f\u3068\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 33.3333%;\">id<\/td>\n<td style=\"width: 33.3333%;\">time<\/td>\n<td style=\"width: 33.3333%;\">value<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">-1.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">1.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">2.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">1.8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u307e\u305f\u3001\u6b21\u306e\u3088\u3046\u306a pandas.DataFrame\u3082 tsfresh\u3067\u7c21\u5358\u306b\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p><strong>A)\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304c\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u00d7\u6642\u9593\u8ef8\u3067\u3042\u308b\u30c7\u30fc\u30bf\u3067\u8907\u6570\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u6301\u3064 pandas.DataFrame<\/strong>\uff08\u4f8b\u3048\u3070\u30013\u8ef8\u52a0\u901f\u5ea6\u30bb\u30f3\u30b5\u30fc\u306e\u305d\u308c\u305e\u308c\u306e\u52a0\u901f\u5ea6\u5909\u5316\u91cfX, X, Z\uff09<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 20%;\">id<\/td>\n<td style=\"width: 20%;\">time<\/td>\n<td style=\"width: 20%;\">X<\/td>\n<td style=\"width: 20%;\">Y<\/td>\n<td style=\"width: 20%;\">Z<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">A<\/td>\n<td style=\"width: 20%;\">1<\/td>\n<td style=\"width: 20%;\">0.1<\/td>\n<td style=\"width: 20%;\">0.5<\/td>\n<td style=\"width: 20%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">A<\/td>\n<td style=\"width: 20%;\">2<\/td>\n<td style=\"width: 20%;\">-0.3<\/td>\n<td style=\"width: 20%;\">0.7<\/td>\n<td style=\"width: 20%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">A<\/td>\n<td style=\"width: 20%;\">3<\/td>\n<td style=\"width: 20%;\">0.5<\/td>\n<td style=\"width: 20%;\">0.5<\/td>\n<td style=\"width: 20%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">B<\/td>\n<td style=\"width: 20%;\">1<\/td>\n<td style=\"width: 20%;\">0.2<\/td>\n<td style=\"width: 20%;\">0.6<\/td>\n<td style=\"width: 20%;\">0.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">B<\/td>\n<td style=\"width: 20%;\">2<\/td>\n<td style=\"width: 20%;\">0.3<\/td>\n<td style=\"width: 20%;\">0.7<\/td>\n<td style=\"width: 20%;\">0.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 20%;\">B<\/td>\n<td style=\"width: 20%;\">3<\/td>\n<td style=\"width: 20%;\">-0.4<\/td>\n<td style=\"width: 20%;\">0.4<\/td>\n<td style=\"width: 20%;\">-0.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>B)\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304c\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u00d7\u6642\u9593\u8ef8\u00d7\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u306e\u30ab\u30e9\u30e0\u3067\u3042\u308b pandas.DataFrame<\/strong>\uff08\u4e0a\u8868\u306e X, Y, Z\u3092\u7e26\u6301\u3061\u5316\u3057\u305f\u3082\u306e\uff09<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 25%;\">id<\/td>\n<td style=\"width: 25%;\">time<\/td>\n<td style=\"width: 25%;\">key<\/td>\n<td style=\"width: 25%;\">value<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">0.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">A<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">X<\/td>\n<td style=\"width: 25%;\">-0.4<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.6<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">Y<\/td>\n<td style=\"width: 25%;\">0.4<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">1<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">0.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">2<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">0.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 25%;\">B<\/td>\n<td style=\"width: 25%;\">3<\/td>\n<td style=\"width: 25%;\">Z<\/td>\n<td style=\"width: 25%;\">-0.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>C)pandas.DataFrame\u3092\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u3054\u3068\u306b\u8f9e\u66f8\u3068\u3057\u3066\u683c\u7d0d\u3057\u305f Dict<\/strong><\/p>\n<p>{ &#8220;X&#8221;:<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 33.3333%;\">id<\/td>\n<td style=\"width: 33.3333%;\">time<\/td>\n<td style=\"width: 33.3333%;\">value<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">-0.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>,&#8221;Y&#8221;:<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 33.3333%;\">id<\/td>\n<td style=\"width: 33.3333%;\">time<\/td>\n<td style=\"width: 33.3333%;\">value<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">0.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">0.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.6<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">0.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">0.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>,&#8221;Z&#8221;:<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 33.3333%;\">id<\/td>\n<td style=\"width: 33.3333%;\">time<\/td>\n<td style=\"width: 33.3333%;\">value<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">A<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">-0.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">1<\/td>\n<td style=\"width: 33.3333%;\">0.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">2<\/td>\n<td style=\"width: 33.3333%;\">0.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\">B<\/td>\n<td style=\"width: 33.3333%;\">3<\/td>\n<td style=\"width: 33.3333%;\">-0.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>}<\/p>\n<p>\u3053\u308c\u3089\u306e\u30c7\u30fc\u30bf\u3092\u6574\u5f62\u3059\u308b\u306e\u306f Python\u3067\u3042\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002\u3057\u304b\u3057\u3001\u5927\u91cf\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u7d71\u5408\u3057\u3066\u7e26\u6301\u3061\u5316\u3059\u308b\u306e\u3067 Python\u306e Pandas\u3067\u3084\u3063\u3066\u3057\u307e\u3046\u306e\u304c\u30aa\u30b9\u30b9\u30e1\u3067\u3059\u3002Jupyter notebook\u3084 Jupyter Lab\u9650\u5b9a\u3067\u3059\u304c Pandas\u3092\u30ce\u30fc\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3067\u4f7f\u3048\u308b\u30c4\u30fc\u30eb\u3082\u958b\u767a\u3055\u308c\u3066\u3044\u308b\u306e\u3067\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u306b\u81ea\u4fe1\u304c\u306a\u3044\u4eba\u306f\u305d\u3061\u3089\u3092\u4f75\u7528\u3059\u308b\u306e\u304c\u826f\u3044\u304b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n<p>Pandas \u3092\u30ce\u30fc\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3067\u6574\u5f62\u3067\u304d\u308b\u30c4\u30fc\u30eb bamboolib<\/p>\n<ul>\n<li><a href=\"https:\/\/bamboolib.8080labs.com\/\" target=\"_blank\" rel=\"noopener\">\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/tkrabel\/bamboolib\" target=\"_blank\" rel=\"noopener\">GitHub<\/a><\/li>\n<\/ul>\n<p>\u203b\u3053\u3061\u3089\u306e\u30c4\u30fc\u30eb\u306f\u30ed\u30fc\u30ab\u30eb\u74b0\u5883\u3067\u3057\u304b\u7121\u6599\u3067\u4f7f\u3048\u306a\u3044\u306e\u3067\u305d\u306e\u70b9\u306f\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2 id=\"rtoc-4\" >\u7279\u5fb4\u91cf\u751f\u6210<\/h2>\n<p>\u4e0a\u8a18\u306e\u30c7\u30fc\u30bf\u304c\u7528\u610f\u3067\u304d\u305f\u3089\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3044\u307e\u3057\u3087\u3046\u3002\u3084\u308b\u3053\u3068\u306fPython\u3067 tsfresh\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066 <a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/api\/tsfresh.feature_extraction.html#module-tsfresh.feature_extraction.extraction\" target=\"_blank\" rel=\"noopener\">tsfresh.feature_extraction.extract_features<\/a>\u00a0\u95a2\u6570\u3092\u5b9f\u884c\u3059\u308b\u3060\u3051\u3067\u3059\u3002\u00a0\u3053\u308c\u3092\u5b9f\u884c\u3059\u308b\u3068\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u00d7\u7d04800\u500b\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306b\u95a2\u3059\u308b\u7279\u5fb4\u91cf\u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002<\/p>\n<p>\u5b9f\u884c\u6642\u9593\u306f\u30c7\u30fc\u30bf\u91cf\u306b\u3088\u308a\u307e\u3059\u304c\u3001400\u500b\u306eID\u306b\u5bfe\u3057\u3066\u3001\u5404\u3005\u304c200~300\u7a0b\u5ea6\u306e\u9577\u3055\u3092\u6301\u3064\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3067\u306f\u7d0440\u79d2\u3067\u751f\u6210\u3067\u304d\u307e\u3057\u305f\u3002<\/p>\n<p>\u3053\u3053\u3067\u6ce8\u610f\u3057\u306a\u3044\u3068\u3044\u3051\u306a\u3044\u306e\u306f\u7528\u610f\u3057\u305f\u30c7\u30fc\u30bf\u306e\u69cb\u9020\u3067 \u00a0tsfresh.feature_extraction.extract_features\u5185\u306e\u5909\u6570\u6307\u5b9a\u304c\u5fae\u5999\u306b\u5909\u308f\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n<p><strong>A)\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304c\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u00d7\u6642\u9593\u8ef8\u3067\u3042\u308b\u30c7\u30fc\u30bf\u3067\u8907\u6570\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u6301\u3064 pandas.DataFrame\u306e\u5834\u5408<\/strong><\/p>\n<pre class=\"language-python\"><code>from tsfresh.feature_extraction import extract_features\r\n\r\nfeatures = extract_features(\r\n    timeseries_container=dataframe,\r\n    default_fc_parameters=None,\r\n    column_id='id',\r\n    column_sort='time',\r\n    column_kind=None,\r\n    column_value=None\r\n)<\/code><\/pre>\n<div class=\"jin-yohaku30\"><\/div>\n<p><strong>B)\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304c\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u00d7\u6642\u9593\u8ef8\u00d7\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u306e\u30ab\u30e9\u30e0\u3067\u3042\u308b pandas.DataFrame\u306e\u5834\u5408<\/strong><br \/>\ncolumn_kind\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u306e\u30ab\u30e9\u30e0\u540d\u3092\u3001column_value\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u5024\u306e\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<pre class=\"language-python\"><code>from tsfresh.feature_extraction import extract_features\r\n\r\nfeatures = extract_features(\r\n    timeseries_container=dataframe,\r\n    default_fc_parameters=None,\r\n    column_id='id',\r\n    column_sort='time',\r\n    column_kind='key',\r\n    column_value='value'\r\n)<\/code><\/pre>\n<div class=\"jin-yohaku30\"><\/div>\n<p><strong>C)pandas.DataFrame\u3092\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u3054\u3068\u306b\u8f9e\u66f8\u3068\u3057\u3066\u683c\u7d0d\u3057\u305f Dict\u306e\u5834\u5408<\/strong><br \/>\nB\u3068\u306f\u9055\u3063\u3066column_kind\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7a2e\u985e\u306e\u30ab\u30e9\u30e0\u540d\u6307\u5b9a\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n<pre class=\"language-python\"><code>from tsfresh.feature_extraction import extract_features\r\n\r\nfeatures = extract_features(\r\n    timeseries_container=dataframe,\r\n    default_fc_parameters=None,\r\n    column_id='id',\r\n    column_sort='time',\r\n    column_kind=None,\r\n    column_value='value'\r\n)<\/code><\/pre>\n<p>\u3044\u305a\u308c\u306e\u30b1\u30fc\u30b9\u3067\u3082\u751f\u6210\u3055\u308c\u308b\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306f\u6b21\u306e\u3088\u3046\u306a\u3001\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304cid\u3067\u3001\u30ab\u30e9\u30e0\u306b\u7279\u5fb4\u91cf\u3092\u6301\u3064\u3082\u306e\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<table style=\"border-collapse: collapse; width: 100%;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 10%;\">id<\/td>\n<td style=\"width: 10%;\">X_feature_1<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">X_feature_n<\/td>\n<td style=\"width: 10%;\">Y_feature_1<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">Y_feature_n<\/td>\n<td style=\"width: 10%;\">Z_feature_1<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">Z_feature_n<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 10%;\">A<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 10%;\">B<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<td style=\"width: 10%;\">&#8230;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 id=\"rtoc-5\" >\u7279\u5fb4\u91cf\u9078\u629e<\/h2>\n<p>\u751f\u6210\u3057\u305f\u7279\u5fb4\u91cf\u3092\u4f7f\u3063\u3066\u6a5f\u68b0\u5b66\u7fd2\u3092\u884c\u3046\u5834\u5408\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u826f\u304f\u3059\u308b\u305f\u3081\u306b\u6b21\u306e\u5de5\u592b\u304c\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\u6b20\u640d\u5024\u3001\u7570\u5e38\u5024\u306e\u51e6\u7406<\/li>\n<li>\u76ee\u7684\u5909\u6570\u3068\u76f8\u95a2\u6027\u304c\u3042\u308b\u7279\u5fb4\u91cf\u3092\u9078\u629e<\/li>\n<\/ul>\n<p>tsfresh\u306b\u306f\u3053\u308c\u3092\u81ea\u52d5\u3067\u51e6\u7406\u3067\u304d\u308b\u95a2\u6570\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li>\u6b20\u640d\u5024\u3001\u7570\u5e38\u5024\uff08\u7121\u9650\u5927\uff09\u3092\u51e6\u7406\u3059\u308b\u95a2\u6570 <a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/api\/tsfresh.utilities.html#tsfresh.utilities.dataframe_functions.impute\" target=\"_blank\" rel=\"noopener\">tsfresh.utilities.dataframe_functions.impute<\/a><\/li>\n<\/ul>\n<pre class=\"language-python\"><code>from tsfresh.utilities.dataframe_functions import impute\r\n\r\nimpute(features)<\/code><\/pre>\n<ul>\n<li>\u76ee\u7684\u5909\u6570\u3068\u76f8\u95a2\u6027\u306e\u3042\u308b\u7279\u5fb4\u91cf\u3092\u9078\u629e\u3059\u308b\u95a2\u6570 <a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/api\/tsfresh.feature_selection.html#module-tsfresh.feature_selection.selection\" target=\"_blank\" rel=\"noopener\">tsfresh.feature_selection.selection<\/a><\/li>\n<\/ul>\n<p>\u76ee\u7684\u5909\u6570\u306f\u5404ID\u3054\u3068\u306b\u4e0e\u3048\u3089\u308c\u305f\u6b63\u89e3\u30e9\u30d9\u30eb\u3092\u95a2\u6570\u5185\u306e\u5909\u6570y\u3067\u6307\u5b9a\u3057\u307e\u3059\u3002\u6b63\u89e3\u30e9\u30d9\u30eb\u304c3\u7a2e\u985e\u4ee5\u4e0a\u3042\u308b\u3088\u3046\u306a\u30de\u30eb\u30c1\u30af\u30e9\u30b9\u5206\u985e\u3067\u306f multiclass\u3092True\u3068\u3057\u3001n_significant\u306b[\u30af\u30e9\u30b9\u6570-1]\u306e\u6574\u6570\u3092\u6307\u5b9a\u3057\u307e\u3059<\/p>\n<pre class=\"language-python\"><code>from tsfresh import select_features\r\n\r\nfeatures_filter = select_features(\r\n    X=features,\r\n    y=target,\r\n    multiclass=False,\r\n    n_significant=None\r\n)<\/code><\/pre>\n<p>\u3053\u308c\u3089\u3092\u5b9f\u884c\u3059\u308b\u3068\u7279\u5fb4\u91cf\u306e\u6570\u304c\u5927\u5e45\u306b\u6e1b\u308a\u3001\u3059\u306a\u308f\u3061\u6b21\u5143\u306e\u546a\u3044\u304c\u7de9\u548c\u3055\u308c\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e\u8a55\u4fa1\u30b9\u30b3\u30a2\u304c\u826f\u304f\u306a\u308b\u30b1\u30fc\u30b9\u304c\u591a\u3044\u3067\u3059\u3002\u305f\u3060\u3057\u3001\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u7279\u5fb4\u91cf\u304c\u5168\u3066\u6d88\u3048\u3066\u3057\u307e\u3046\u306e\u3067\u4e00\u5ea6\u5b9f\u884c\u3057\u3066\u307f\u3066\u304b\u3089\u5b9f\u969b\u306b\u4f7f\u3046\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u306e\u304c\u826f\u3044\u304b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n<p>\u3061\u306a\u307f\u306b\u3001\u7279\u5fb4\u91cf\u306e\u751f\u6210\u304b\u3089\u76ee\u7684\u5909\u6570\u306b\u5bfe\u3057\u3066\u306e\u9078\u5225\u307e\u3067\u3092\u4e00\u3064\u306e\u95a2\u6570\u3067\u5b9f\u884c\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002<\/p>\n<pre class=\"language-python\"><code>from tsfresh import extract_relevant_features\r\n\r\nfeatures = extract_relevant_features(\r\n    timeseries_container=dataframe,\r\n    y=target,\r\n    default_fc_parameters=None,\r\n    column_id='id',\r\n    column_sort='time',\r\n    column_kind=None,\r\n    column_value=None\r\n)<\/code><\/pre>\n<h2 id=\"rtoc-6\" >\u751f\u6210\u3059\u308b\u7279\u5fb4\u91cf\u3092\u9078\u5225<\/h2>\n<p>\u5b9f\u969b\u306e\u904b\u7528\u3067\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3046\u3068\u304d\u306b\u306f\u6bce\u56de800\u500b\u3082\u306e\u7279\u5fb4\u91cf\u3092\u751f\u6210\u3059\u308b\u306e\u3067\u306f\u306a\u304f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306b\u5fc5\u8981\u306a\u3082\u306e\u306e\u307f\u3092\u751f\u6210\u3057\u305f\u304f\u306a\u308b\u3068\u601d\u3044\u307e\u3059\u3002tsfresh\u306b\u306f\u305d\u306e\u305f\u3081\u306e\u6a5f\u80fd\u3082\u5f53\u7136\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u307e\u305a\u3001\u7279\u5fb4\u91cf\u751f\u6210\u2192\u9078\u5225\u307e\u3067\u3092\u884c\u3063\u305f pandas.DateFrame\u306b\u5bfe\u3057\u3066\u6b8b\u3063\u3066\u3044\u308b\u7279\u5fb4\u91cf\u30ab\u30e9\u30e0\u306e\u8f9e\u66f8\u3092\u751f\u6210\u3057\u307e\u3059\u3002\uff08<a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/api\/tsfresh.feature_extraction.html#tsfresh.feature_extraction.settings.from_columns\" target=\"_blank\" rel=\"noopener\">tsfresh.feature_extraction.settings.from_columns<\/a>\uff09<\/p>\n<pre class=\"language-python\"><code>from tsfresh.feature_extraction.settings import from_columns\r\n\r\nsettings = from_columns(features)<\/code><\/pre>\n<p>\u3053\u308c\u3067\u751f\u6210\u3055\u308c\u305f\u8f9e\u66f8\u3092\u7279\u5fb4\u91cf\u751f\u6210\u306e\u969b\u306b default_fc_parameters\u3067\u6307\u5b9a\u3059\u308c\u3070\u9078\u5225\u6e08\u306e\u7279\u5fb4\u91cf\u306e\u307f\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002<\/p>\n<pre class=\"language-python\"><code>from tsfresh.feature_extraction import extract_features\r\n\r\nselected_features = extract_features(\r\n    timeseries_container=dataframe,\r\n    default_fc_parameters=setting,\r\n    column_id='id',\r\n    column_sort='time',\r\n    column_kind=None,\r\n    column_value=None\r\n)<\/code><\/pre>\n<h2 id=\"rtoc-7\" >\u79fb\u52d5\u7a93\u3054\u3068\u306e\u7279\u5fb4\u91cf\u751f\u6210<\/h2>\n<p>\u6642\u7cfb\u5217\u3092\u4e00\u5b9a\u533a\u9593\u7a93\u3054\u3068\u306b\u53d6\u308a\u51fa\u3057\u3066\u5c11\u3057\u5148\u306e\u72b6\u614b\u3092\u4e88\u6e2c\u3059\u308b\u3068\u3044\u3046\u624b\u6cd5\u304c\u591a\u304f\u306e\u6642\u7cfb\u5217\u4e88\u6e2c\u30e2\u30c7\u30eb\u3067\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u307e\u3067\u7d39\u4ecb\u3057\u3066\u304d\u305f\u30b3\u30fc\u30c9\u306f\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u5168\u4f53\u3092\u4f7f\u3063\u3066\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3046\u3082\u306e\u3067\u3057\u305f\u304c\u3001\u3053\u308c\u3092\u500b\u5225\u306eID\u3067\u79fb\u52d5\u7a93\u3054\u3068\u306b\u884c\u3048\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u3092\u6574\u5f62\u3059\u308b\u95a2\u6570\u304c tsfresh\u306b\u3042\u308a\u307e\u3059\uff08<a href=\"https:\/\/tsfresh.readthedocs.io\/en\/latest\/api\/tsfresh.utilities.html#tsfresh.utilities.dataframe_functions.roll_time_series\" target=\"_blank\" rel=\"noopener\">tsfresh.utilities.dataframe_functions.roll_time_series<\/a>\uff09\u3002<\/p>\n<p>\u5909\u6570\u306e min_timeshift\u3068 max_timeshift\u3067\u306f\u79fb\u52d5\u7a93\u5e45\u306e\u9577\u3055\u3092\u3069\u3053\u304b\u3089\u3001\u3069\u3053\u307e\u3067\u3092\u8a08\u7b97\u3057\u3066\u3088\u3044\u304b\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/p>\n<pre class=\"language-python\"><code>from tsfresh.utilities.dataframe_functions import roll_time_series\r\n\r\ndf_rolled = roll_time_series(\r\n    df_or_dict=dataframe,\r\n    column_id='id',\r\n    column_sort='time',\r\n    max_timeshift=None,\r\n    min_timeshift=0\r\n)<\/code><\/pre>\n<p>\u3053\u308c\u3067\u751f\u6210\u3055\u308c\u305f pandas.DateFrame\u306b\u306fid\u306e\u30ab\u30e9\u30e0\u306b\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u306b\u52a0\u3048\u3066\u3069\u306e\u79fb\u52d5\u7a93\u3067\u3042\u308b\u304b\u306e\u756a\u53f7\u3082\u4ed8\u4e0e\u3055\u308c\u3066\u3001\u30d7\u30e9\u30a4\u30de\u30ea\u30fc\u30ad\u30fc\u304cID\u00d7\u79fb\u52d5\u7a93\u3068\u306a\u308a\u307e\u3059\u3002\u306a\u306e\u3067\u3001\u3053\u308c\u3092\u4f7f\u3063\u3066\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3046\u3053\u3068\u3067\u3001\u30e6\u30cb\u30fc\u30af\u756a\u53f7\u304b\u3064\u79fb\u52d5\u7a93\u3054\u3068\u306e\u7279\u5fb4\u91cf\u751f\u6210\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\uff08\u305f\u3060\u3057\u3001\u884c\u6570\u304c\u7206\u5897\u3059\u308b\u306e\u3067\u5b9f\u884c\u6642\u9593\u306b\u306f\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\uff09\u3002<\/p>\n<p>\u3068\u3044\u3046\u3053\u3068\u3067 tsfresh\u3067\u3067\u304d\u308b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u7279\u5fb4\u91cf\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u3092\u307e\u3068\u3081\u3066\u307f\u307e\u3057\u305f\u3002<br \/>\n\u672c\u30d6\u30ed\u30b0\u3067\u306f\u3053\u306e tsfresh\u3068\u5f0a\u793e\u306e\u81ea\u52d5\u6a5f\u68b0\u5b66\u7fd2\u30c4\u30fc\u30eb\u3067\u3042\u308b 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