{"id":48944,"date":"2024-11-08T19:12:41","date_gmt":"2024-11-08T10:12:41","guid":{"rendered":"https:\/\/wordpress.realcollector.jp\/blog\/?p=48944"},"modified":"2024-11-08T19:12:41","modified_gmt":"2024-11-08T10:12:41","slug":"%e3%83%ab%e3%83%83%e3%82%af%e3%83%90%e3%83%83%e3%82%af-12","status":"publish","type":"post","link":"https:\/\/wordpress.realcollector.jp\/blog\/?p=48944","title":{"rendered":"\u30eb\u30c3\u30af\u30d0\u30c3\u30af"},"content":{"rendered":"<p>\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u3084\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u5206\u91ce\u3067\u983b\u7e41\u306b\u4f7f\u7528\u3055\u308c\u308b\u624b\u6cd5\u3067\u3059\u3002\u3053\u306e\u624b\u6cd5\u306f\u3001\u30e2\u30c7\u30eb\u306e\u8a13\u7df4\u4e2d\u306b\u904e\u53bb\u306e\u60c5\u5831\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u826f\u3044\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002<\/p>\n<p>\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306f\u3001\u4e3b\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3084\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u306b\u5229\u7528\u3055\u308c\u307e\u3059\u3002\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3068\u306f\u3001\u6642\u9593\u306e\u7d4c\u904e\u306b\u4f34\u3063\u3066\u5909\u52d5\u3059\u308b\u30c7\u30fc\u30bf\u306e\u3053\u3068\u3067\u3042\u308a\u3001\u682a\u4fa1\u3084\u6c17\u8c61\u30c7\u30fc\u30bf\u306a\u3069\u304c\u8a72\u5f53\u3057\u307e\u3059\u3002\u7cfb\u5217\u30c7\u30fc\u30bf\u306f\u3001\u7279\u5b9a\u306e\u9806\u5e8f\u306b\u5f93\u3063\u3066\u51fa\u73fe\u3059\u308b\u30c7\u30fc\u30bf\u306e\u3053\u3068\u3067\u3042\u308a\u3001\u6587\u7ae0\u3084\u97f3\u58f0\u306a\u3069\u304c\u8a72\u5f53\u3057\u307e\u3059\u3002<\/p>\n<p>\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u3067\u306f\u3001\u73fe\u5728\u306e\u30c7\u30fc\u30bf\u3092\u4e88\u6e2c\u3059\u308b\u969b\u306b\u3001\u904e\u53bb\u306e\u8907\u6570\u306e\u30c7\u30fc\u30bf\u3092\u5165\u529b\u3068\u3057\u3066\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306f\u904e\u53bb\u306e\u30d1\u30bf\u30fc\u30f3\u3084\u30c8\u30ec\u30f3\u30c9\u3092\u5b66\u7fd2\u3057\u3001\u672a\u6765\u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306e\u7bc4\u56f2\uff08\u904e\u53bb\u306e\u30c7\u30fc\u30bf\u306e\u671f\u9593\uff09\u306f\u3001\u4f7f\u7528\u3055\u308c\u308b\u30c7\u30fc\u30bf\u306e\u6027\u8cea\u306b\u3088\u3063\u3066\u7570\u306a\u308a\u307e\u3059\u304c\u3001\u4e00\u822c\u7684\u306b\u306f\u6570\u65e5\u304b\u3089\u6570\u9031\u9593\u7a0b\u5ea6\u3067\u3059\u3002<\/p>\n<p>\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306e\u7279\u5fb4\u3092\u4ee5\u4e0b\u306b\u30ea\u30b9\u30c8\u30a2\u30c3\u30d7\u3057\u307e\u3059\uff1a 1. \u904e\u53bb\u306e\u30c7\u30fc\u30bf\u3092\u5229\u7528\u3059\u308b\u305f\u3081\u3001\u6642\u9593\u306e\u7d4c\u904e\u3084\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u5909\u52d5\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u6027\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002 2. \u30e2\u30c7\u30eb\u306f\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3084\u7cfb\u5217\u30c7\u30fc\u30bf\u306b\u7279\u5316\u3057\u3066\u304a\u308a\u3001\u305d\u306e\u6027\u8cea\u306b\u5408\u308f\u305b\u305f\u5b66\u7fd2\u3092\u884c\u3046\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002 3. \u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306e\u7bc4\u56f2\u3092\u8abf\u6574\u3059\u308b\u3053\u3068\u3067\u3001\u4e88\u6e2c\u306e\u7cbe\u5ea6\u3092\u8abf\u6574\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u7bc4\u56f2\u3092\u5e83\u3052\u308c\u3070\u3001\u904e\u53bb\u306e\u30c8\u30ec\u30f3\u30c9\u3092\u3088\u308a\u8a73\u7d30\u306b\u5b66\u7fd2\u3067\u304d\u307e\u3059\u304c\u3001\u30e2\u30c7\u30eb\u306e\u8907\u96d1\u3055\u3082\u5897\u3057\u307e\u3059\u3002 4. \u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306f\u3001\u4ed6\u306e\u624b\u6cd5\u3068\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u7528\u3059\u308b\u3053\u3068\u3082\u53ef\u80fd\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\uff08Convolutional Neural Network\uff09\u3068\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u753b\u50cf\u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u306b\u3082\u5fdc\u7528\u3067\u304d\u307e\u3059\u3002 5. \u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306f\u3001\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u5206\u91ce\u306b\u304a\u3044\u3066\u5e83\u304f\u7814\u7a76\u3055\u308c\u3066\u304a\u308a\u3001\u591a\u304f\u306e\u95a2\u9023\u8ad6\u6587\u3084\u7814\u7a76\u304c\u5b58\u5728\u3057\u307e\u3059\u3002<\/p>\n<p>\u4ee5\u4e0b\u306b\u3001\u30eb\u30c3\u30af\u30d0\u30c3\u30af\u306b\u95a2\u9023\u3059\u308b5\u3064\u306e\u53c2\u8003\u6587\u732e\u3092\u7d39\u4ecb\u3057\u307e\u3059\uff1a 1. &#8220;Long Short-Term Memory Neural Network for Regression Problems with Time Series Data.&#8221; (<a href=\"https:\/\/ieeexplore.ieee.org\/document\/9073195\">https:\/\/ieeexplore.ieee.org\/document\/9073195<\/a>) 2. &#8220;Time Series Prediction Using Recurrent Neural Networks (LSTMs).&#8221; (<a href=\"https:\/\/towardsdatascience.com\/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6b7831a\">https:\/\/towardsdatascience.com\/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6b7831a<\/a>) 3. &#8220;Look Back and Predict Forward: A Sequential Rank-Sensitive Recommendation Framework with Rank-Level Attentional Factor Model.&#8221; (<a href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3357384.3358010\">https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3357384.3358010<\/a>) 4. &#8220;Forecasting Stock Prices Using Long Short-Term Memory Recurrent Neural Networks.&#8221; (<a href=\"https:\/\/www.mdpi.com\/2071-1050\/11\/9\/2652\">https:\/\/www.mdpi.com\/2071-1050\/11\/9\/2652<\/a>) 5. &#8220;Deep Learning for Time-Series Analysis: A Comparative Review.&#8221; (<a href=\"https:\/\/arxiv.org\/abs\/1802.04431\">https:\/\/arxiv.org\/abs\/1802.04431<\/a>)<\/p>\n<p><a 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