{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T16:40:22Z","timestamp":1648572022929},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2003,12]]},"abstract":"<jats:p> In this paper we propose a simple forecasting strategy which exploits the multiresolution property of the wavelet transform. US aggregate retail sales data have strong trend and seasonal patterns, providing a good testing ground for the proposed forecasting method. First a wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Then a forecasting engine (neural network or fuzzy inference system) is trained on each of the relevant resolution scales, and individual wavelet scale forecasts are recombined to form the overall forecast. Substantial information in both the dynamic nonlinear trend and seasonal patterns of the time series is efficiently exploited: we choose short past windows for the inputs to the forecasting engines at lower scales and long past windows at higher scales. The forecasting engines learn the mapping hierarchically: using a scale-recursive strategy, we combine only those scales where significant events are detected. Univariate simulation results on US aggregate retailing indicate that the proposed method fares favourably in relation to forecasting results obtained by training a neural network on original time series. Multivariate simulation results obtained by including structural components inflation, recession, interest rates, unemployment, show improvement in sales-trend forecast. <\/jats:p>","DOI":"10.1142\/s0219691303000281","type":"journal-article","created":{"date-parts":[[2003,12,5]],"date-time":"2003-12-05T04:20:48Z","timestamp":1070598048000},"page":"449-463","source":"Crossref","is-referenced-by-count":2,"title":["MULTIRESOLUTION FORECASTING FOR US RETAILING USING WAVELET DECOMPOSITIONS"],"prefix":"10.1142","volume":"01","author":[{"given":"ASHWANI","family":"KUMAR","sequence":"first","affiliation":[{"name":"ABV-Indian Institute of Information Technology  and Management, Gwalior, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D. P.","family":"AGRAWAL","sequence":"additional","affiliation":[{"name":"Union Public Services Commission,  New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. D.","family":"JOSHI","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Delhi,  New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,1,5]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1109\/72.165591"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690390108"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1109\/91.917126"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1142\/9789812811332"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1109\/72.728396"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1109\/72.935090"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1016\/S0969-6989(00)00011-4"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511841040"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/34.192463"}],"container-title":["International Journal of Wavelets, Multiresolution and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219691303000281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T17:02:28Z","timestamp":1565110948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219691303000281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,12]]},"references-count":10,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2012,1,5]]},"published-print":{"date-parts":[[2003,12]]}},"alternative-id":["10.1142\/S0219691303000281"],"URL":"https:\/\/doi.org\/10.1142\/s0219691303000281","relation":{},"ISSN":["0219-6913","1793-690X"],"issn-type":[{"value":"0219-6913","type":"print"},{"value":"1793-690X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,12]]}}}