{"id":479331,"date":"2023-08-09T10:33:53","date_gmt":"2023-08-09T10:33:53","guid":{"rendered":""},"modified":"2023-09-05T11:18:37","modified_gmt":"2023-09-05T11:18:37","slug":"time-series-decomposition","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/time-series-decomposition\/","title":{"rendered":"\u6642\u7cfb\u5217\u5206\u89e3"},"content":{"rendered":"<p>\u6642\u7cfb\u5217\u5206\u89e3\u3068\u306f\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf 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20 \u4e16\u7d00\u521d\u982d\u3001\u7279\u306b WS \u30b8\u30a7\u30f4\u30a9\u30f3\u30ba\u3084\u30b5\u30a4\u30e2\u30f3 \u30af\u30ba\u30cd\u30c3\u30c4\u306a\u3069\u306e\u7d4c\u6e08\u5b66\u8005\u306e\u7814\u7a76\u306b\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30a2\u30a4\u30c7\u30a2\u306f 1920 \u5e74\u4ee3\u3068 1930 \u5e74\u4ee3\u306b\u30a6\u30a7\u30ba\u30ea\u30fc C. \u30df\u30c3\u30c1\u30a7\u30eb\u306a\u3069\u306e\u7d4c\u6e08\u5b66\u8005\u306b\u3088\u3063\u3066\u3055\u3089\u306b\u767a\u5c55\u3057\u307e\u3057\u305f\u3002\u305d\u306e\u76ee\u7684\u306f\u3001\u7d4c\u6e08\u30c7\u30fc\u30bf\u306e\u5468\u671f\u7684\u306a\u52d5\u304d\u3092\u50be\u5411\u3084\u305d\u306e\u4ed6\u306e\u5909\u52d5\u304b\u3089\u5206\u96e2\u3059\u308b\u3053\u3068\u3067\u3057\u305f\u3002<\/p>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306b\u95a2\u3059\u308b\u8a73\u7d30\u60c5\u5831\u3002\u30c8\u30d4\u30c3\u30af\u306e\u62e1\u5f35 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\u4e0d\u898f\u5247\u306a\u9593\u9694\u3067\u767a\u751f\u3059\u308b\u5909\u52d5\u3002\u591a\u304f\u306e\u5834\u5408\u3001\u7d4c\u6e08\u30b5\u30a4\u30af\u30eb\u306b\u95a2\u9023\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<li><strong>\u4e0d\u898f\u5247<\/strong>: \u30c7\u30fc\u30bf\u5185\u306e\u30e9\u30f3\u30c0\u30e0\u307e\u305f\u306f\u4e88\u6e2c\u4e0d\u53ef\u80fd\u306a\u52d5\u304d\u3002<\/li>\n<\/ul>\n<p>\u5206\u89e3\u306f\u3001\u79fb\u52d5\u5e73\u5747\u3001\u6307\u6570\u5e73\u6ed1\u6cd5\u3001ARIMA \u306a\u3069\u306e\u7d71\u8a08\u30e2\u30c7\u30ea\u30f3\u30b0\u306a\u3069\u306e\u3055\u307e\u3056\u307e\u306a\u65b9\u6cd5\u3092\u901a\u3058\u3066\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306e\u5185\u90e8\u69cb\u9020\u3002\u6642\u7cfb\u5217\u5206\u89e3\u306e\u4ed5\u7d44\u307f<\/h2>\n<p>\u6642\u7cfb\u5217\u5206\u89e3\u306f\u3001\u7cfb\u5217\u306e\u3055\u307e\u3056\u307e\u306a\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u3092\u5206\u96e2\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u6a5f\u80fd\u3057\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u30c8\u30ec\u30f3\u30c9\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8<\/strong>: \u591a\u304f\u306e\u5834\u5408\u3001\u79fb\u52d5\u5e73\u5747\u307e\u305f\u306f\u6307\u6570\u5e73\u6ed1\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u62bd\u51fa\u3055\u308c\u307e\u3059\u3002<\/li>\n<li><strong>\u5b63\u7bc0\u8981\u7d20<\/strong>: \u4e00\u5b9a\u671f\u9593\u5185\u306e\u7e70\u308a\u8fd4\u3057\u30d1\u30bf\u30fc\u30f3\u3092\u8b58\u5225\u3059\u308b\u3053\u3068\u3067\u691c\u51fa\u3055\u308c\u307e\u3059\u3002<\/li>\n<li><strong>\u5faa\u74b0\u7684\u8981\u7d20<\/strong>: \u4e0d\u898f\u5247\u306a\u9593\u9694\u3067\u767a\u751f\u3059\u308b\u5909\u52d5\u3092\u5206\u6790\u3059\u308b\u3053\u3068\u3067\u8b58\u5225\u3055\u308c\u307e\u3059\u3002<\/li>\n<li><strong>\u4e0d\u898f\u5247\u306a\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8<\/strong>: \u4ed6\u306e\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u3092\u62bd\u51fa\u3057\u305f\u5f8c\u306b\u6b8b\u308b\u3082\u306e\u3002\u591a\u304f\u306e\u5834\u5408\u3001\u30ce\u30a4\u30ba\u307e\u305f\u306f\u30a8\u30e9\u30fc\u3068\u3057\u3066\u6271\u308f\u308c\u307e\u3059\u3002<\/li>\n<\/ol>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306e\u4e3b\u306a\u7279\u5fb4\u306e\u5206\u6790<\/h2>\n<ul>\n<li><strong>\u6b63\u78ba\u3055<\/strong>: \u3088\u308a\u6b63\u78ba\u306a\u4e88\u6e2c\u3068\u7406\u89e3\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/li>\n<li><strong>\u591a\u7528\u9014\u6027<\/strong>\u7d4c\u6e08\u3001\u91d1\u878d\u3001\u74b0\u5883\u79d1\u5b66\u306a\u3069\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u306b\u5fdc\u7528\u3067\u304d\u307e\u3059\u3002<\/li>\n<li><strong>\u8907\u96d1<\/strong>: \u9ad8\u5ea6\u306a\u7d71\u8a08\u624b\u6cd5\u3068\u5c02\u9580\u77e5\u8b58\u304c\u5fc5\u8981\u306b\u306a\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n<\/ul>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306e\u7a2e\u985e<\/h2>\n<p>\u4e3b\u306b2\u3064\u306e\u30bf\u30a4\u30d7\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ol>\n<li><strong>\u52a0\u6cd5\u30e2\u30c7\u30eb<\/strong>\n<ul>\n<li>\u30c8\u30ec\u30f3\u30c9 + \u5b63\u7bc0\u6027 + \u5468\u671f\u6027 + \u4e0d\u898f\u5247\u6027<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u4e57\u6cd5\u30e2\u30c7\u30eb<\/strong>\n<ul>\n<li>\u30c8\u30ec\u30f3\u30c9 \u00d7 \u5b63\u7bc0 \u00d7 \u5468\u671f \u00d7 \u4e0d\u898f\u5247<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<table>\n<thead>\n<tr>\n<th>\u30bf\u30a4\u30d7<\/th>\n<th>\u306b\u9069\u3057<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6dfb\u52a0\u5264<\/td>\n<td>\u7dda\u5f62\u50be\u5411\u3068\u5b63\u7bc0\u5909\u52d5<\/td>\n<\/tr>\n<tr>\n<td>\u4e57\u6cd5<\/td>\n<td>\u6307\u6570\u95a2\u6570\u7684\u306a\u50be\u5411\u3068\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u306e\u5909\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306e\u4f7f\u3044\u65b9\u3001\u4f7f\u7528\u306b\u4f34\u3046\u554f\u984c\u3068\u305d\u306e\u89e3\u6c7a\u7b56<\/h2>\n<h3>\u7528\u9014<\/h3>\n<ul>\n<li>\u5c06\u6765\u306e\u50be\u5411\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/li>\n<li>\u6839\u672c\u7684\u306a\u30d1\u30bf\u30fc\u30f3\u3092\u7279\u5b9a\u3059\u308b\u3002<\/li>\n<li>\u7570\u5e38\u3092\u691c\u51fa\u3057\u307e\u3059\u3002<\/li>\n<\/ul>\n<h3>\u554f\u984c\u3068\u89e3\u6c7a\u7b56<\/h3>\n<ul>\n<li><strong>\u904e\u5b66\u7fd2<\/strong>: \u904e\u5ea6\u306b\u8907\u96d1\u306a\u30e2\u30c7\u30eb\u306e\u4f7f\u7528\u306f\u907f\u3051\u3066\u304f\u3060\u3055\u3044\u3002<\/li>\n<li><strong>\u30c7\u30fc\u30bf\u54c1\u8cea\u306e\u554f\u984c<\/strong>: \u30c7\u30fc\u30bf\u304c\u30af\u30ea\u30fc\u30f3\u3067\u3042\u308a\u3001\u9069\u5207\u306b\u6e96\u5099\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/li>\n<\/ul>\n<h2>\u4e3b\u306a\u7279\u5fb4\u3068\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u6027<\/th>\n<th>\u6642\u7cfb\u5217\u5206\u89e3<\/th>\n<th>\u30d5\u30fc\u30ea\u30a8\u89e3\u6790<\/th>\n<th>\u30a6\u30a7\u30fc\u30d6\u30ec\u30c3\u30c8\u89e3\u6790<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u96c6\u4e2d<\/td>\n<td>\u30c8\u30ec\u30f3\u30c9\u3001\u5b63\u7bc0<\/td>\n<td>\u983b\u5ea6<\/td>\n<td>\u6642\u9593\u3068\u5468\u6ce2\u6570<\/td>\n<\/tr>\n<tr>\n<td>\u8907\u96d1<\/td>\n<td>\u9069\u5ea6<\/td>\n<td>\u8907\u96d1\u306a<\/td>\n<td>\u975e\u5e38\u306b\u8907\u96d1<\/td>\n<\/tr>\n<tr>\n<td>\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3<\/td>\n<td>\u7d4c\u6e08\u3001\u30d3\u30b8\u30cd\u30b9<\/td>\n<td>\u4fe1\u53f7\u51e6\u7406<\/td>\n<td>\u753b\u50cf\u89e3\u6790<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u6642\u7cfb\u5217\u5206\u89e3\u306b\u95a2\u3059\u308b\u4eca\u5f8c\u306e\u5c55\u671b\u3068\u6280\u8853<\/h2>\n<p>\u5c06\u6765\u306e\u5c55\u671b\u3068\u3057\u3066\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u6280\u8853\u306e\u7d71\u5408\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u5206\u6790\u3001\u6642\u7cfb\u5217\u5206\u89e3\u306e\u81ea\u52d5\u5316\u306a\u3069\u304c\u6319\u3052\u3089\u308c\u307e\u3059\u3002<\/p>\n<h2>\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092\u6642\u7cfb\u5217\u5206\u89e3\u306b\u4f7f\u7528\u307e\u305f\u306f\u95a2\u9023\u4ed8\u3051\u308b\u65b9\u6cd5<\/h2>\n<p>OneProxy \u306e\u3088\u3046\u306a\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u6642\u7cfb\u5217\u5206\u6790\u306e\u305f\u3081\u306e\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0 \u30c7\u30fc\u30bf\u306e\u53ce\u96c6\u3092\u5bb9\u6613\u306b\u3057\u307e\u3059\u3002\u3055\u307e\u3056\u307e\u306a\u30aa\u30f3\u30e9\u30a4\u30f3 \u30bd\u30fc\u30b9\u304b\u3089\u30c7\u30fc\u30bf\u3092\u5b89\u5168\u304b\u3064\u533f\u540d\u3067\u30b9\u30af\u30ec\u30a4\u30d4\u30f3\u30b0\u3067\u304d\u308b\u305f\u3081\u3001\u5206\u6790\u306e\u305f\u3081\u306e\u8c4a\u5bcc\u3067\u591a\u69d8\u306a\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u304c\u78ba\u4fdd\u3055\u308c\u307e\u3059\u3002<\/p>\n<h2>\u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n<ul>\n<li><a href=\"https:\/\/oneproxy.pro\/jp\/\" target=\"_new\" rel=\"noopener\">OneProxy \u30a6\u30a7\u30d6\u30b5\u30a4\u30c8<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Time_series\" target=\"_new\" rel=\"noopener nofollow\">\u6642\u7cfb\u5217\u5206\u6790 \u2013 Wikipedia<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/introduction-to-time-series-forecasting-30e0ead32c72\" target=\"_new\" rel=\"noopener nofollow\">\u6642\u7cfb\u5217\u4e88\u6e2c\u5165\u9580 \u2013 \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306b\u5411\u3051\u3066<\/a><\/li>\n<\/ul>\n<p>\u3053\u308c\u3089\u306e\u30ea\u30f3\u30af\u3067\u306f\u3001\u6642\u7cfb\u5217\u5206\u89e3\u3068\u95a2\u9023\u30c6\u30af\u30ce\u30ed\u30b8\u30fc\u306b\u95a2\u3059\u308b\u3088\u308a\u8a73\u7d30\u306a\u60c5\u5831\u304c\u63d0\u4f9b\u3055\u308c\u307e\u3059\u3002<\/p>","protected":false},"featured_media":470691,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479331","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Time Series Decomposition<\/mark>","faq_items":[{"question":"What is Time Series Decomposition?","answer":"<p>Time series decomposition is the process of breaking down a time series data set into its constituent parts, typically including trend, seasonal, cyclical, and irregular or random components. Analyzing these components separately can provide valuable insights into the underlying structure of the data.<\/p>"},{"question":"What are the key components of Time Series Decomposition?","answer":"<p>The key components of time series decomposition are the Trend, Seasonal, Cyclical, and Irregular components. The trend shows long-term movements, seasonal reveals repeating patterns, cyclical identifies fluctuations at irregular intervals, and the irregular component accounts for random movements.<\/p>"},{"question":"What are the main types of Time Series Decomposition?","answer":"<p>There are two primary types of time series decomposition: the Additive Model, where components are added together (Trend + Seasonal + Cyclical + Irregular), and the Multiplicative Model, where components are multiplied (Trend \u00d7 Seasonal \u00d7 Cyclical \u00d7 Irregular).<\/p>"},{"question":"How is Time Series Decomposition used in forecasting?","answer":"<p>Time series decomposition is used in forecasting by separating the underlying components of the data. By understanding these components, analysts can make more accurate predictions about future trends and patterns.<\/p>"},{"question":"What problems can be encountered with Time Series Decomposition, and how can they be solved?","answer":"<p>Problems that can be encountered with time series decomposition include overfitting and data quality issues. Overfitting can be avoided by not using overly complex models, and data quality issues can be mitigated by ensuring that the data is clean and well-prepared.<\/p>"},{"question":"What is the relationship between proxy servers like OneProxy and Time Series Decomposition?","answer":"<p>Proxy servers like OneProxy can be associated with time series decomposition by facilitating the collection of real-time data for analysis. They enable secure and anonymous scraping of data from various sources, ensuring a rich and diverse data set for decomposition and analysis.<\/p>"},{"question":"What are the future perspectives related to Time Series Decomposition?","answer":"<p>Future perspectives related to time series decomposition include the integration of machine learning techniques, real-time analysis, and automation. These advancements may lead to more sophisticated and efficient methods for analyzing time series data.<\/p>"},{"question":"How can I learn more about Time Series Decomposition?","answer":"<p>You can learn more about time series decomposition by visiting resources such as the OneProxy website, Wikipedia's page on time series analysis, and various data science blogs and tutorials. The related links section of the article provides direct links to these resources.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/479331","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/479331\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/470691"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=479331"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}