{"id":12840,"date":"2023-04-08T15:14:40","date_gmt":"2023-04-08T15:14:40","guid":{"rendered":"https:\/\/www.goodacademic.com\/blog\/questions\/similarity-between-how-time-series-decomposition-and-winters-exponential-smoothing-deal-with-seasonality\/"},"modified":"2023-04-08T15:14:40","modified_gmt":"2023-04-08T15:14:40","slug":"similarity-between-how-time-series-decomposition-and-winters-exponential-smoothing-deal-with-seasonality","status":"publish","type":"questions","link":"https:\/\/www.goodacademic.com\/blog\/questions\/similarity-between-how-time-series-decomposition-and-winters-exponential-smoothing-deal-with-seasonality\/","title":{"rendered":"similarity between how time-series decomposition and Winters\u2019 exponential smoothing deal with seasonality."},"content":{"rendered":"

1. Explain the similarity between how time-series decomposition and Winters\u2019 exponential smoothing deal with seasonality.<\/p>\n

2. Using your own words, write a description of each of the four components (1) the trend; 2) seasonality; 3) the cycle; and 4) random (irregular) fluctuations)of the classic time series decomposition technique. Avoid using mathematical relationships and technical jargon as much as possible so that your explanations can be understood by almost anyone.\n<\/div>\n
3. Explain why a combined model might be better than any of the original contributing models. <\/div>\n
<\/div>\n
1. Your assignment should be 2 – 3 pages in length and written in APA style format.\n<\/div>\n
2. Separate title and reference page (minimum of 3 or 4 reference)\n<\/div>\n
3. Double spaced with 12 point Times Roman font and 1\u201d by 1.5\u201d margins.\n<\/div>\n
4. Paraphrasing of content \u2013 Demonstrate that you understand the case by summarizing the case in your own words. Direct quotes should be used minimally.<\/div>\n","protected":false},"excerpt":{"rendered":"

1. Explain the similarity between how time-series decomposition and Winters\u2019 exponential smoothing deal with seasonality. 2. Using your own words, write a description of each of the four components (1) the trend; 2) seasonality; 3) the cycle; and 4) random (irregular) fluctuations)of the classic time series decomposition technique. Avoid using mathematical relationships and technical jargon […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"disciplines":[1112],"paper_types":[],"tagged":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/12840"}],"collection":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions"}],"about":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/types\/questions"}],"author":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/comments?post=12840"}],"version-history":[{"count":0,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/questions\/12840\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/media?parent=12840"}],"wp:term":[{"taxonomy":"disciplines","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/disciplines?post=12840"},{"taxonomy":"paper_types","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/paper_types?post=12840"},{"taxonomy":"tagged","embeddable":true,"href":"https:\/\/www.goodacademic.com\/blog\/wp-json\/wp\/v2\/tagged?post=12840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}