\u76ee\u5f55<\/p> \n
- \n
- \u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60\uff1aseq2seq \u6a21\u578b\u9884\u6d4b\u592a\u9633\u9ed1\u5b50<\/a>\n
- \n
- \u5b66\u4e60\u8def\u7ebf<\/a><\/li> \n
- \u5546\u4e1a\u4e2d\u7684\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/a>\n
- \n
- \u5546\u4e1a\u4e2d\u5e94\u7528\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/a><\/li> \n <\/ul><\/li> \n
- \u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff1a\u4f7f\u7528
keras<\/code> \u9884\u6d4b\u592a\u9633\u9ed1\u5b50<\/a><\/li> \n
- \u9012\u5f52\u795e\u7ecf\u7f51\u7edc<\/a><\/li> \n
- \u8bbe\u7f6e\u3001\u9884\u5904\u7406\u4e0e\u63a2\u7d22<\/a>\n
- \n
- \u6240\u7528\u7684\u5305<\/a><\/li> \n
- \u6570\u636e<\/a><\/li> \n
- \u63a2\u7d22\u6027\u6570\u636e\u5206\u6790<\/a><\/li> \n
- \u56de\u6d4b\uff1a\u65f6\u95f4\u5e8f\u5217\u4ea4\u53c9\u9a8c\u8bc1<\/a><\/li> \n <\/ul><\/li> \n
- LSTM \u6a21\u578b<\/a>\n
- \n
- \u6570\u636e\u51c6\u5907<\/a><\/li> \n
- \u7528
recipe<\/code> \u505a\u6570\u636e\u9884\u5904\u7406<\/a><\/li> \n
- \u8c03\u6574\u6570\u636e\u5f62\u72b6<\/a><\/li> \n
- \u6784\u5efa LSTM \u6a21\u578b<\/a><\/li> \n
- \u5728\u6240\u6709\u5206\u5272\u4e0a\u56de\u6d4b\u6a21\u578b<\/a><\/li> \n <\/ul><\/li> \n <\/ul><\/li> \n <\/ul> \n <\/div> \n <\/div> \n
\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60\uff1aseq2seq \u6a21\u578b\u9884\u6d4b\u592a\u9633\u9ed1\u5b50<\/h1> \n
\n
\u672c\u6587\u7ffb\u8bd1\u81ea\u300aTime Series Deep Learning, Part 2: Predicting Sunspot Frequency With Keras Lstm in R\u300b\uff0c\u7565\u6709\u5220\u51cf<\/p> \n
\u539f\u6587\u94fe\u63a5<\/a><\/p> \n <\/blockquote> \n
\u6df1\u5ea6\u5b66\u4e60\u4e8e\u5546\u4e1a<\/strong>\u7684\u7528\u9014\u4e4b\u4e00\u662f\u63d0\u9ad8\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u7684\u51c6\u786e\u6027\u3002\u4e4b\u524d\u7684\u6559\u7a0b<\/a>\u663e\u793a\u4e86\u5982\u4f55\u5229\u7528\u81ea\u76f8\u5173\u6027<\/em>\u9884\u6d4b\u672a\u6765 10 \u5e74\u7684\u6708\u5ea6\u592a\u9633\u9ed1\u5b50\u6570\u91cf\u3002\u672c\u6559\u7a0b\u5c06\u501f\u52a9 RStudio \u91cd\u65b0\u5ba1\u89c6\u592a\u9633\u9ed1\u5b50\u6570\u636e\u96c6\uff0c\u5e76\u4e14\u4f7f\u7528\u5230 TensorFlow for R<\/a> \u4e2d\u4e00\u4e9b\u9ad8\u7ea7\u7684\u6df1\u5ea6\u5b66\u4e60\u529f\u80fd\uff0c\u5c55\u793a\u57fa\u4e8e
keras<\/code><\/a> \u7684\u6df1\u5ea6\u5b66\u4e60\u6559\u7a0b\u9047\u5230
tfruns<\/code>\uff08\u7528\u4e8e\u8ffd\u8e2a\u3001\u53ef\u89c6\u5316\u548c\u7ba1\u7406 TensorFlow \u8bad\u7ec3\u3001\u5b9e\u9a8c\u7684\u4e00\u6574\u5957\u5de5\u5177<\/a>\uff09\u540e\u4ea7\u751f\u51fa\u7684\u6709\u8da3\u7ed3\u679c\u3002<\/p> \n
\u5b66\u4e60\u8def\u7ebf<\/h2> \n
\u8be5\u6df1\u5ea6\u5b66\u4e60\u6559\u7a0b<\/strong>\u5c06\u6559\u4f1a\u4f60\uff1a<\/p> \n
- \n
- \u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60\u5982\u4f55\u5e94\u7528\u4e8e\u5546\u4e1a<\/li> \n
- \u6df1\u5ea6\u5b66\u4e60\u9884\u6d4b\u592a\u9633\u9ed1\u5b50<\/li> \n
- \u5982\u4f55\u5efa\u7acb LSTM \u6a21\u578b<\/li> \n
- \u5982\u4f55\u56de\u6d4b LSTM \u6a21\u578b<\/li> \n <\/ul> \n
\u4e8b\u5b9e\u4e0a\uff0c\u6700\u9177\u7684\u4e8b\u4e4b\u4e00\u662f\u4f60\u80fd\u753b\u51fa LSTM \u9884\u6d4b\u7684\u56de\u6d4b\u7ed3\u679c\u3002<\/p> \n
<\/p> \n
\u5546\u4e1a\u4e2d\u7684\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/h2> \n
\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u662f\u5546\u4e1a\u4e2d\u5b9e\u73b0\u6295\u8d44\u56de\u62a5\u7387\uff08ROI\uff09\u7684\u4e00\u4e2a\u5173\u952e\u9886\u57df\u3002\u60f3\u4e00\u60f3\uff1a\u9884\u6d4b\u51c6\u786e\u5ea6\u63d0\u9ad8 10\uff05 \u5c31\u53ef\u4ee5\u4e3a\u673a\u6784\u8282\u7701\u6570\u767e\u4e07\u7f8e\u5143<\/strong>\u3002\u8fd9\u600e\u4e48\u53ef\u80fd\uff1f\u4e0b\u9762\u8ba9\u6211\u4eec\u6765\u770b\u770b\u3002<\/p> \n
\u6211\u4eec\u5c06\u4ee5 NVIDIA<\/a> \u4e3a\u4f8b\uff0c\u4e00\u5bb6\u4e3a Artificial Intelligence<\/em> \u548c Deep Learning<\/em> \u751f\u4ea7\u6700\u5148\u8fdb\u82af\u7247\u7684\u534a\u5bfc\u4f53\u5382\u5546\u3002NVIDIA<\/em> \u751f\u4ea7\u7684\u56fe\u5f62\u5904\u7406\u5668\u6216 GPU<\/a>\uff0c\u8fd9\u5bf9\u4e8e\u9ad8\u6027\u80fd\u6df1\u5ea6\u5b66\u4e60\u6240\u8981\u6c42\u7684\u5927\u89c4\u6a21\u6570\u503c\u8ba1\u7b97\u6765\u8bf4\u662f\u5fc5\u9700\u7684\u3002\u82af\u7247\u770b\u8d77\u6765\u50cf\u8fd9\u6837\u3002<\/p> \n
<\/p> \n
\u4e0e\u6240\u6709\u5236\u9020\u5546\u4e00\u6837\uff0cNVIDIA<\/em> \u9700\u8981\u9884\u6d4b\u5176\u4ea7\u54c1\u7684\u9700\u6c42<\/strong>\u3002\u4e3a\u4ec0\u4e48\uff1f \u56e0\u4e3a\u4ed6\u4eec\u636e\u6b64\u53ef\u4ee5\u4e3a\u5ba2\u6237\u63d0\u4f9b\u5408\u9002\u6570\u91cf\u7684\u82af\u7247\u3002\u8fd9\u4e2a\u9884\u6d4b\u5f88\u5173\u952e\uff0c\u9700\u8981\u5f88\u591a\u6280\u5de7\u548c\u4e00\u4e9b\u8fd0\u6c14\u624d\u80fd\u505a\u5230\u8fd9\u4e00\u70b9\u3002<\/p> \n
\u6211\u4eec\u6240\u8ba8\u8bba\u7684\u662f\u9500\u552e\u9884\u6d4b<\/strong>\uff0c\u5b83\u63a8\u52a8\u4e86 NVIDIA<\/em> \u505a\u51fa\u7684\u6240\u6709\u751f\u4ea7\u5236\u9020\u51b3\u7b56\u3002\u8fd9\u5305\u62ec\u8d2d\u4e70\u591a\u5c11\u539f\u6750\u6599\uff0c\u6709\u591a\u5c11\u4eba\u6765\u5236\u9020\u82af\u7247\uff0c\u4ee5\u53ca\u9700\u8981\u591a\u5c11\u9884\u7b97\u7528\u4e8e\u52a0\u5de5\u548c\u88c5\u914d\u64cd\u4f5c\u3002\u9500\u552e\u9884\u6d4b\u4e2d\u7684\u9519\u8bef\u8d8a\u591a\uff0cNVIDIA<\/em> \u4ea7\u751f\u7684\u6210\u672c\u5c31\u8d8a\u5927\uff0c\u56e0\u4e3a\u6240\u6709\u8fd9\u4e9b\u6d3b\u52a8\uff08\u4f9b\u5e94\u94fe\u3001\u5e93\u5b58\u7ba1\u7406\u3001\u8d22\u52a1\u89c4\u5212\u7b49\uff09\u90fd\u4f1a\u53d8\u5f97\u6ca1\u6709\u610f\u4e49\uff01<\/p> \n
\u5546\u4e1a\u4e2d\u5e94\u7528\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/h3> \n
\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/strong>\u5bf9\u4e8e\u9884\u6d4b\u5177\u6709\u9ad8\u81ea\u76f8\u5173\u6027\u7684\u6570\u636e\u975e\u5e38\u51c6\u786e\uff0c\u56e0\u4e3a\u5305\u62ec LSTM<\/a> \u548c GRU<\/a> \u5728\u5185\u7684\u7b97\u6cd5\u53ef\u4ee5\u4ece\u5e8f\u5217\u4e2d\u5b66\u4e60\u4fe1\u606f\uff0c\u65e0\u8bba\u6a21\u5f0f\u4f55\u65f6\u53d1\u751f\u3002\u8fd9\u4e9b\u7279\u6b8a\u7684 RNN<\/a> \u65e8\u5728\u5177\u6709\u957f\u671f\u8bb0\u5fc6\u6027\uff0c\u8fd9\u610f\u5473\u7740\u5b83\u4eec\u5584\u4e8e\u5728\u6700\u8fd1\u53d1\u751f\u7684\u89c2\u5bdf\u548c\u5f88\u4e45\u4e4b\u524d\u53d1\u751f\u7684\u89c2\u5bdf\u4e4b\u95f4\u5b66\u4e60\u6a21\u5f0f\u3002\u8fd9\u4f7f\u5b83\u4eec\u975e\u5e38\u9002\u5408\u65f6\u95f4\u5e8f\u5217\uff01\u4f46\u5b83\u4eec\u5bf9\u9500\u552e\u6570\u636e\u6709\u7528\u5417\uff1f\u4e5f\u8bb8\uff0c\u6765\uff01\u6211\u4eec\u8ba8\u8bba\u4e00\u4e0b\u3002<\/p> \n
\u9500\u552e\u6570\u636e\u6df7\u5408\u4e86\u5404\u79cd\u7279\u5f81\uff0c\u4f46\u901a\u5e38\u6709\u5b63\u8282\u6027\u6a21\u5f0f<\/strong>\u548c\u8d8b\u52bf<\/strong>\u3002\u8d8b\u52bf<\/strong>\u53ef\u4ee5\u662f\u5e73\u5766\u7684\u3001\u7ebf\u6027\u7684\u3001\u6307\u6570\u7684\u7b49\u7b49\u3002\u8fd9\u901a\u5e38\u4e0d\u662f LSTM \u64c5\u957f\u7684\u5730\u65b9\uff0c\u4f46\u5176\u4ed6\u4f20\u7edf\u7684\u9884\u6d4b\u65b9\u6cd5\u53ef\u4ee5\u68c0\u6d4b\u8d8b\u52bf\u3002\u4f46\u662f\uff0c\u5b63\u8282\u6027\u4e0d\u540c\u3002\u9500\u552e\u6570\u636e\u4e2d\u7684\u5b63\u8282\u6027<\/strong>\u662f\u4e00\u79cd\u53ef\u4ee5\u5728\u591a\u4e2a\u9891\u7387\uff08\u5e74\u5ea6\u3001\u5b63\u5ea6\u3001\u6708\u5ea6\u3001\u5468\u5ea6\u751a\u81f3\u6bcf\u5929\uff09\u4e0a\u51fa\u73b0\u7684\u6a21\u5f0f\u3002LSTM \u975e\u5e38\u9002\u5408\u68c0\u6d4b\u5b63\u8282\u6027\uff0c\u56e0\u4e3a\u5b83\u901a\u5e38\u5177\u6709\u81ea\u76f8\u5173\u6027\u3002\u56e0\u6b64\uff0cLSTM \u548c GRU \u53ef\u4ee5\u5f88\u597d\u5730\u5e2e\u52a9\u6539\u8fdb\u5b63\u8282\u6027\u68c0\u6d4b\uff0c\u4ece\u800c\u51cf\u5c11\u9500\u552e\u9884\u6d4b<\/strong>\u4e2d\u7684\u6574\u4f53\u9884\u6d4b\u8bef\u5dee\u3002<\/p> \n
\u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff1a\u4f7f\u7528
keras<\/code> \u9884\u6d4b\u592a\u9633\u9ed1\u5b50<\/h2> \n
\u8fd9\u4e00\u8282\u6211\u4eec\u5c06\u501f\u52a9\u57fa\u672c\u7684 R \u5de5\u5177\u5728\u592a\u9633\u9ed1\u5b50<\/a>\u6570\u636e\u96c6\u4e0a\u505a\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002\u592a\u9633\u9ed1\u5b50\u662f\u592a\u9633\u8868\u9762\u7684\u4f4e\u6e29\u533a\u57df\uff0c\u8fd9\u91cc\u6709\u4e00\u5f20\u6765\u81ea NASA \u7684\u592a\u9633\u9ed1\u5b50\u56fe\u7247\u3002<\/p> \n
<\/p> \n
\u6211\u4eec\u4f7f\u7528\u6708\u5ea6\u6570\u636e\u96c6
sunspots.month<\/code>\uff08\u4e5f\u6709\u4e00\u4e2a\u5e74\u5ea6\u9891\u7387\u7684\u7248\u672c\uff09\uff0c\u5b83\u5305\u62ec 265 \u5e74\u95f4\uff081749 - 2013\uff09\u7684\u6708\u5ea6\u592a\u9633\u9ed1\u5b50\u89c2\u6d4b\u3002<\/p> \n
<\/p> \n
\u9884\u6d4b\u8be5\u6570\u636e\u96c6\u5177\u6709\u76f8\u5f53\u7684\u6311\u6218\u6027\uff0c\u56e0\u4e3a\u77ed\u671f\u5185\u7684\u9ad8\u53d8\u5f02\u6027\u4ee5\u53ca\u957f\u671f\u5185\u660e\u663e\u7684\u4e0d\u89c4\u5219\u5468\u671f\u6027\u3002\u4f8b\u5982\uff0c\u4f4e\u9891\u5468\u671f\u8fbe\u5230\u7684\u6700\u5927\u5e45\u5ea6\u5dee\u5f02\u5f88\u5927\uff0c\u8fbe\u5230\u6700\u5927\u4f4e\u9891\u5468\u671f\u9ad8\u5ea6\u6240\u9700\u7684\u9ad8\u9891\u5468\u671f\u6b65\u6570\u4e5f\u662f\u5982\u6b64\u3002\uff08\u8bd1\u6ce8\uff1a\u6570\u636e\u4e2d\u7684\u5c40\u90e8\u9ad8\u70b9\u4e4b\u95f4\u95f4\u9694\u5927\u7ea6\u4e3a 11 \u5e74\uff09<\/p> \n
\u6211\u4eec\u7684\u6587\u7ae0\u5c06\u91cd\u70b9\u5173\u6ce8\u4e24\u4e2a\u4e3b\u8981\u65b9\u9762\uff1a\u5982\u4f55\u5c06\u6df1\u5ea6\u5b66\u4e60\u5e94\u7528\u4e8e\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff0c\u4ee5\u53ca\u5982\u4f55\u5728\u8be5\u9886\u57df\u4e2d\u6b63\u786e\u5e94\u7528\u4ea4\u53c9\u9a8c\u8bc1\u3002\u5bf9\u4e8e\u540e\u8005\uff0c\u6211\u4eec\u5c06\u4f7f\u7528
rsample<\/code><\/a> \u5305\u6765\u5141\u8bb8\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u91cd\u65b0\u62bd\u6837\u3002\u5bf9\u4e8e\u524d\u8005\uff0c\u6211\u4eec\u7684\u76ee\u6807\u4e0d\u662f\u8fbe\u5230\u6700\u4f73\u8868\u73b0\uff0c\u800c\u662f\u663e\u793a\u4f7f\u7528\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u5bf9\u6b64\u7c7b\u6570\u636e\u8fdb\u884c\u5efa\u6a21\u65f6\u7684\u4e00\u822c\u64cd\u4f5c\u8fc7\u7a0b\u3002<\/p> \n
\u9012\u5f52\u795e\u7ecf\u7f51\u7edc<\/h2> \n
\u5f53\u6211\u4eec\u7684\u6570\u636e\u5177\u6709\u5e8f\u5217\u7ed3\u6784\u65f6\uff0c\u6211\u4eec\u5c31\u7528\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\u8fdb\u884c\u5efa\u6a21\u3002<\/p> \n
\u76ee\u524d\u4e3a\u6b62\uff0c\u5728 RNN \u4e2d\uff0c\u5efa\u7acb\u7684\u6700\u4f73\u67b6\u6784\u662f GRU\uff08\u95e8\u9012\u5f52\u5355\u5143\uff09\u548c LSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\uff09\u3002\u4eca\u5929\uff0c\u6211\u4eec\u4e0d\u8981\u653e\u5927\u5b83\u4eec\u81ea\u8eab\u72ec\u7279\u7684\u4e1c\u897f\uff0c\u800c\u662f\u96c6\u4e2d\u4e8e\u5b83\u4eec\u4e0e\u6700\u7cbe\u7b80\u7684 RNN \u7684\u5171\u540c\u70b9\u4e0a\uff1a\u57fa\u672c\u7684\u9012\u5f52\u7ed3\u6784\u3002<\/p> \n
\u4e0e\u901a\u5e38\u79f0\u4e3a\u591a\u5c42\u611f\u77e5\u5668\uff08MLP\uff09\u7684\u795e\u7ecf\u7f51\u7edc\u7684\u539f\u578b\u76f8\u6bd4\uff0cRNN \u5177\u6709\u968f\u65f6\u95f4\u63a8\u79fb\u7684\u72b6\u6001\u3002\u6765\u81ea Goodfellow \u7b49\u4eba\u7684\u8457\u4f5c<\/a>\u2014\u2014\u201c\u6df1\u5ea6\u5b66\u4e60\u7684\u5723\u7ecf\u201d\uff0c\u4ece\u8fd9\u4e2a\u56fe\u4e2d\u53ef\u4ee5\u5f88\u597d\u5730\u770b\u51fa\u8fd9\u4e00\u70b9\uff1a<\/p> \n
<\/p> \n
\u6bcf\u6b21\uff0c\u72b6\u6001\u662f\u5f53\u524d\u8f93\u5165\u548c\u5148\u524d\u9690\u542b\u72b6\u6001\u7684\u7ec4\u5408\u3002\u8fd9\u8ba9\u4eba\u8054\u60f3\u5230\u81ea\u56de\u5f52\u6a21\u578b\uff0c\u4f46\u662f\u5bf9\u4e8e\u795e\u7ecf\u7f51\u7edc\uff0c\u6211\u4eec\u5fc5\u987b\u5728\u67d0\u79cd\u7a0b\u5ea6\u4e0a\u505c\u6b62\u4f9d\u8d56\u3002<\/p> \n
\u56e0\u4e3a\u90a3\u662f\u4e3a\u4e86\u786e\u5b9a\u6743\u91cd\uff0c\u6211\u4eec\u4f1a\u4e0d\u65ad\u8ba1\u7b97\u8f93\u5165\u53d8\u5316\u540e\u6211\u4eec\u7684\u635f\u5931\u5982\u4f55\u53d8\u5316\u3002\u73b0\u5728\uff0c\u5982\u679c\u6211\u4eec\u5fc5\u987b\u8003\u8651\u7684\u8f93\u5165\u5728\u4efb\u610f\u65f6\u95f4\u6b65\u65e0\u9650\u5730\u8fd4\u56de\uff0c\u90a3\u4e48\u6211\u4eec\u5c06\u65e0\u6cd5\u8ba1\u7b97\u6240\u6709\u8fd9\u4e9b\u68af\u5ea6\u3002\u7136\u800c\uff0c\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u7684\u9690\u542b\u72b6\u6001\u5c06\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u901a\u8fc7\u56fa\u5b9a\u6570\u91cf\u7684\u6b65\u9aa4\u7ee7\u7eed\u524d\u8fdb\u3002<\/p> \n
\u4e00\u65e6\u6211\u4eec\u52a0\u8f7d\u5e76\u9884\u5904\u7406\u6570\u636e\uff0c\u6211\u4eec\u5c31\u4f1a\u56de\u8fc7\u5934\u6765\u3002<\/p> \n
\u8bbe\u7f6e\u3001\u9884\u5904\u7406\u4e0e\u63a2\u7d22<\/h2> \n
\u6240\u7528\u7684\u5305<\/h3> \n
\u8fd9\u91cc\u662f\u8be5\u6559\u7a0b\u6240\u6d89\u53ca\u5230\u7684\u5305\u3002<\/p> \n
# Core Tidyverse\nlibrary(tidyverse)\nlibrary(glue)\nlibrary(forcats)\n\n# Time Series\nlibrary(timetk)\nlibrary(tidyquant)\nlibrary(tibbletime)\n\n# Visualization\nlibrary(cowplot)\n\n# Preprocessing\nlibrary(recipes)\n\n# Sampling \/ Accuracy\nlibrary(rsample)\nlibrary(yardstick)\n\n# Modeling\nlibrary(keras)\nlibrary(tfruns)<\/code><\/pre> \n
\u5982\u679c\u4f60\u4e4b\u524d\u6ca1\u6709\u5728 R \u4e2d\u8fd0\u884c\u8fc7 Keras\uff0c\u4f60\u9700\u8981\u7528
install_keras()<\/code> \u51fd\u6570\u5b89\u88c5 Keras\u3002<\/p> \n
# Install Keras if you have not installed before\ninstall_keras()<\/code><\/pre> \n
\u6570\u636e<\/h3> \n
\u6570\u636e\u96c6
sunspot.month<\/code> \u662f\u4e00\u4e2a
ts<\/code> \u7c7b\u5bf9\u8c61\uff08\u975e
tidy<\/code> \u7c7b\uff09\uff0c\u6240\u4ee5\u6211\u4eec\u5c06\u4f7f\u7528
timetk<\/code> \u4e2d\u7684
tk_tbl()<\/code> \u51fd\u6570\u8f6c\u6362\u4e3a
tidy<\/code> \u6570\u636e\u96c6\u3002\u6211\u4eec\u4f7f\u7528\u8fd9\u4e2a\u51fd\u6570\u800c\u4e0d\u662f\u6765\u81ea
tibble<\/code> \u7684
as.tibble()<\/code>\uff0c\u7528\u6765\u81ea\u52a8\u5c06\u65f6\u95f4\u5e8f\u5217\u7d22\u5f15\u4fdd\u5b58\u4e3a
zoo<\/code>
yearmon<\/code> \u7d22\u5f15\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c06\u4f7f\u7528
lubridate::as_date()<\/code>\uff08\u4f7f\u7528
tidyquant<\/code> \u65f6\u52a0\u8f7d\uff09\u5c06
zoo<\/code> \u7d22\u5f15\u8f6c\u6362\u4e3a\u65e5\u671f\uff0c\u7136\u540e\u8f6c\u6362\u4e3a
tbl_time<\/code> \u5bf9\u8c61\u4ee5\u4f7f\u65f6\u95f4\u5e8f\u5217\u64cd\u4f5c\u8d77\u6765\u66f4\u5bb9\u6613\u3002<\/p> \n
sun_spots <- datasets::sunspot.month %>%\n tk_tbl() %>%\n mutate(index = as_date(index)) %>%\n as_tbl_time(index = index)\n\nsun_spots<\/code><\/pre> \n
# A time tibble: 3,177","orderid":"0","title":"\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60\uff1aseq2seq \u6a21\u578b\u9884\u6d4b\u592a\u9633\u9ed1\u5b50(\u4e00)","smalltitle":"","mid":"0","fname":"R\u8bed\u8a00","special_id":"0","bak_id":"0","info":"0","hits":"654","pages":"11","comments":"0","posttime":"2019-09-03 02:41:26","list":"1567449686","username":"admin","author":"","copyfrom":"","copyfromurl":"","titlecolor":"","fonttype":"0","titleicon":"0","picurl":"https:\/\/www.cppentry.com\/upload_files\/","ispic":"0","yz":"1","yzer":"","yztime":"0","levels":"0","levelstime":"0","keywords":"\u65f6\u95f4\u5e8f\u5217<\/A> \u6df1\u5ea6<\/A> \u5b66\u4e60<\/A> seq2seq<\/A> \u6a21\u578b<\/A> \u9884\u6d4b<\/A> \u592a\u9633\u9ed1\u5b50<\/A>","jumpurl":"","iframeurl":"","style":"","template":"a:3:{s:4:\"head\";s:0:\"\";s:4:\"foot\";s:0:\"\";s:8:\"bencandy\";s:0:\"\";}","target":"0","ip":"120.229.33.54","lastfid":"0","money":"0","buyuser":"","passwd":"","allowdown":"","allowview":"","editer":"","edittime":"0","begintime":"0","endtime":"0","description":"\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60\uff1aseq2seq \u6a21\u578b\u9884\u6d4b\u592a\u9633\u9ed1\u5b50","lastview":"1713584373","digg_num":"0","digg_time":"0","forbidcomment":"0","ifvote":"0","heart":"","htmlname":"","city_id":"0"},"page":"1"}
- \u7528
- \u6570\u636e<\/a><\/li> \n
- \u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\uff1a\u4f7f\u7528
- \u5546\u4e1a\u4e2d\u7684\u65f6\u95f4\u5e8f\u5217\u6df1\u5ea6\u5b66\u4e60<\/a>\n
- \u5b66\u4e60\u8def\u7ebf<\/a><\/li> \n