نتایج جستجو برای: arima فصلی

تعداد نتایج: 7771  

2015
Wei Wu Junqiao Guo Shuyi An Peng Guan Yangwu Ren Linzi Xia Baosen Zhou Hiroshi Nishiura

BACKGROUND Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. METHODS Two hybrid models, one composed of...

2017
Li Luo Le Luo Xinli Zhang Xiaoli He

BACKGROUND Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpa...

ژورنال: مدلسازی اقتصادی 2013
اکبر توکلی مصطفی رجبی مهدی فاضل,

تجربه نشان می‌دهد ادوار تجاری اجتناب ناپذیرند. به دلیل وابستگی تأثیرگذاری سیاست‌های اقتصادی به ادوار تجاری، اقتصاددانان همواره در صدد شناخت نحوه شکل‌گیری ، تأثیرگذاری و پیش‌بینی آن بوده‌اند. مقاله‌ی حاضر با نگاه کوتاهی به مفاهیم حوزه‌ی ادوار تجاری، الگوی خودهمبسته غیرخطی مبتنی بر زنجیره‌های مارکوف (MS-AR) را جهت تحلیل و پیش‌بینی ادوار تجاری ایران معرفی کرده و توانمندی آن را در مقایسه با الگوی خ...

ژورنال: مدلسازی اقتصادی 2013
اکبر توکلی مصطفی رجبی مهدی فاضل,

تجربه نشان می‌دهد ادوار تجاری اجتناب ناپذیرند. به دلیل وابستگی تأثیرگذاری سیاست‌های اقتصادی به ادوار تجاری، اقتصاددانان همواره در صدد شناخت نحوه شکل‌گیری ، تأثیرگذاری و پیش‌بینی آن بوده‌اند. مقاله‌ی حاضر با نگاه کوتاهی به مفاهیم حوزه‌ی ادوار تجاری، الگوی خودهمبسته غیرخطی مبتنی بر زنجیره‌های مارکوف (MS-AR) را جهت تحلیل و پیش‌بینی ادوار تجاری ایران معرفی کرده و توانمندی آن را در مقایسه با الگوی خ...

2001
Konstantinos Kalpakis Dhiral Gada Vasundhara Puttagunta

Many environmental and socioeconomic time–series data can be adequately modeled using Auto-Regressive Integrated Moving Average (ARIMA) models. We call such time–series ARIMA time–series. We consider the problem of clustering ARIMA time–series. We propose the use of the Linear Predictive Coding (LPC) cepstrum of time–series for clustering ARIMA time–series, by using the Euclidean distance betwe...

2014
Wei Ming Yukun Bao Zhongyi Hu Tao Xiong

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead pr...

2002
Thuy Trang T. Nguyen Catherine C. Hood Víctor Gómez

The U.S. Census Bureau has enhanced the X-12-ARIMA seasonal adjustment program by incorporating an improved automatic regARIMA model (regression model with ARIMA errors) selection procedure. Currently this procedure is available only in test version 0.3 of X-12ARIMA, but it will be released in a future version of the program. It is based on the automatic model selection procedure of TRAMO , an ...

2012
Ping Han Pengxin Wang Miao Tian Shuyu Zhang Junming Liu Dehai Zhu

The standardized precipitation index (SPI) was used to quantify the classification of drought in the Guanzhong Plain, China. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecast the SPI series. Most of the selected ARIMA models are seasonal models (SARIMA). The forecast results show that the forecasting power of the ARIMA models increases with the incre...

Journal: :Applied Mathematics and Computation 2005
Chorng-Shyong Ong Jih-Jeng Huang Gwo-Hshiung Tzeng

ARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model estimation and model checking, of which model identification is the most crucial stage in building ARIMA models. However there is no method suitable for both ARIMA and SARIMA that can overcome the problem of local optima....

2015
Amr Mossad Abdulrahman Ali Alazba Ricardo Trigo

Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that a...

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function paginate(evt) { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term pg=parseInt(evt.target.text) var data={ "year":filter_year, "term":term, "pgn":pg } filtered_res=post_and_fetch(data,url) window.scrollTo(0,0); } function update_search_meta(search_meta) { meta_place=document.getElementById("search_meta_data") term=search_meta.term active_pgn=search_meta.pgn num_res=search_meta.num_res num_pages=search_meta.num_pages year=search_meta.year meta_place.dataset.term=term meta_place.dataset.page=active_pgn meta_place.dataset.num_res=num_res meta_place.dataset.num_pages=num_pages meta_place.dataset.year=year document.getElementById("num_result_place").innerHTML=num_res if (year !== "unfilter"){ document.getElementById("year_filter_label").style="display:inline;" document.getElementById("year_filter_place").innerHTML=year }else { document.getElementById("year_filter_label").style="display:none;" document.getElementById("year_filter_place").innerHTML="" } } function update_pagination() { search_meta_place=document.getElementById('search_meta_data') num_pages=search_meta_place.dataset.num_pages; active_pgn=parseInt(search_meta_place.dataset.page); document.getElementById("pgn-ul").innerHTML=""; pgn_html=""; for (i = 1; i <= num_pages; i++){ if (i===active_pgn){ actv="active" }else {actv=""} pgn_li="
  • " +i+ "
  • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->