A New Look at Models For Exponential Smoothing
Exponential smoothing (ES) forecasting methods are widely used but are often discussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising clas...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series D (The Statistician) 2001-01, Vol.50 (2), p.147-159 |
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| container_issue | 2 |
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| container_title | Journal of the Royal Statistical Society. Series D (The Statistician) |
| container_volume | 50 |
| creator | Chatfield, Chris Koehler, Anne B. Ord, J. K. Snyder, Ralph D. |
| description | Exponential smoothing (ES) forecasting methods are widely used but are often discussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising class of dynamic non-linear state space models is described that allows for a changing variance. The richness of possible models helps to explain why ES methods seem to be robust in practice. A modelling approach can enhance the forecaster's ability to identify pertinent components of time series variation, and to obtain more reliable estimates of prediction error variances. The paper should be of particular interest to those engaged in forecasting applications where strategies that allow for risk and uncertainty are needed. |
| doi_str_mv | 10.1111/1467-9884.00267 |
| format | Article |
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K.</creatorcontrib><creatorcontrib>Snyder, Ralph D.</creatorcontrib><title>A New Look at Models For Exponential Smoothing</title><title>Journal of the Royal Statistical Society. Series D (The Statistician)</title><description>Exponential smoothing (ES) forecasting methods are widely used but are often discussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising class of dynamic non-linear state space models is described that allows for a changing variance. The richness of possible models helps to explain why ES methods seem to be robust in practice. A modelling approach can enhance the forecaster's ability to identify pertinent components of time series variation, and to obtain more reliable estimates of prediction error variances. The paper should be of particular interest to those engaged in forecasting applications where strategies that allow for risk and uncertainty are needed.</description><subject>Analytical forecasting</subject><subject>Autoregressive integrated moving average models</subject><subject>Data smoothing</subject><subject>Error rates</subject><subject>Exact sciences and technology</subject><subject>Exponential smoothing</subject><subject>Forecasting</subject><subject>Forecasting models</subject><subject>Holt-Winters method</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Mathematics</subject><subject>Model testing</subject><subject>Modeling</subject><subject>Modelling</subject><subject>Musical intervals</subject><subject>Prediction intervals</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sales forecasting</subject><subject>Sciences and techniques of general use</subject><subject>Seasonality</subject><subject>State space models</subject><subject>Statistical analysis</subject><subject>Statistical forecasts</subject><subject>Statistical models</subject><subject>Statistical variance</subject><subject>Statistics</subject><subject>Structural models</subject><subject>Time series</subject><subject>Time series forecasting</subject><subject>Trend</subject><issn>0039-0526</issn><issn>1467-9884</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><recordid>eNqFkL1PwzAQxS0EEqUwszBkQGxpz05iJ2PpJ1IpEgUxWo5jQ9o0Lnaqtv89KakqNk46nXT3fk-nh9Athg6uq4tDyvwkjsMOAKHsDLVOm3PUAggSHyJCL9GVcwsAzEgUtVCn583U1psas_RE5T2bTBXOGxnrDXdrU6qyykXhzVfGVF95-XmNLrQonLo5zjZ6Hw3f-hN_-jJ-6vemvgwhYH4oY5JqrBgDkkWasBRUIDTWVCYKU5JSkpFMKJbIVNJEaGAsSnQKCQkVi7OgjR4a37U13xvlKr7KnVRFIUplNo4HMYlwgKEWdhuhtMY5qzRf23wl7J5j4Idc-CEFfkiB_-ZSE_dHa-GkKLQVpczdHyykddeysJFt80Lt_3Plr_P5oHG_a7CFq4w9YYTGGJLDu35zzl2ldqezsEtewyziH7MxD0eTxximAz4KfgA2HYfO</recordid><startdate>20010101</startdate><enddate>20010101</enddate><creator>Chatfield, Chris</creator><creator>Koehler, Anne B.</creator><creator>Ord, J. 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K. ; Snyder, Ralph D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4037-4c82bf1e7702d5f27b0e3af1f6c9e162b62d2dae79cbc69af07759fb0924e78d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Analytical forecasting</topic><topic>Autoregressive integrated moving average models</topic><topic>Data smoothing</topic><topic>Error rates</topic><topic>Exact sciences and technology</topic><topic>Exponential smoothing</topic><topic>Forecasting</topic><topic>Forecasting models</topic><topic>Holt-Winters method</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Mathematics</topic><topic>Model testing</topic><topic>Modeling</topic><topic>Modelling</topic><topic>Musical intervals</topic><topic>Prediction intervals</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sales forecasting</topic><topic>Sciences and techniques of general use</topic><topic>Seasonality</topic><topic>State space models</topic><topic>Statistical analysis</topic><topic>Statistical forecasts</topic><topic>Statistical models</topic><topic>Statistical variance</topic><topic>Statistics</topic><topic>Structural models</topic><topic>Time series</topic><topic>Time series forecasting</topic><topic>Trend</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chatfield, Chris</creatorcontrib><creatorcontrib>Koehler, Anne B.</creatorcontrib><creatorcontrib>Ord, J. K.</creatorcontrib><creatorcontrib>Snyder, Ralph D.</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of the Royal Statistical Society. Series D (The Statistician)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chatfield, Chris</au><au>Koehler, Anne B.</au><au>Ord, J. K.</au><au>Snyder, Ralph D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A New Look at Models For Exponential Smoothing</atitle><jtitle>Journal of the Royal Statistical Society. Series D (The Statistician)</jtitle><date>2001-01-01</date><risdate>2001</risdate><volume>50</volume><issue>2</issue><spage>147</spage><epage>159</epage><pages>147-159</pages><issn>0039-0526</issn><eissn>1467-9884</eissn><coden>STTNAP</coden><abstract>Exponential smoothing (ES) forecasting methods are widely used but are often discussed without recourse to a formal statistical framework. This paper reviews and compares a variety of potential models for ES. As well as autoregressive integrated moving average and structural models, a promising class of dynamic non-linear state space models is described that allows for a changing variance. The richness of possible models helps to explain why ES methods seem to be robust in practice. A modelling approach can enhance the forecaster's ability to identify pertinent components of time series variation, and to obtain more reliable estimates of prediction error variances. 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| ispartof | Journal of the Royal Statistical Society. Series D (The Statistician), 2001-01, Vol.50 (2), p.147-159 |
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| language | eng |
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| source | Jstor Complete Legacy; Alma/SFX Local Collection; JSTOR Mathematics & Statistics; EBSCOhost Business Source Complete |
| subjects | Analytical forecasting Autoregressive integrated moving average models Data smoothing Error rates Exact sciences and technology Exponential smoothing Forecasting Forecasting models Holt-Winters method Inference from stochastic processes time series analysis Mathematics Model testing Modeling Modelling Musical intervals Prediction intervals Probability and statistics Regression analysis Sales forecasting Sciences and techniques of general use Seasonality State space models Statistical analysis Statistical forecasts Statistical models Statistical variance Statistics Structural models Time series Time series forecasting Trend |
| title | A New Look at Models For Exponential Smoothing |
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