{"id":16472,"date":"2011-08-12T11:28:44","date_gmt":"2011-08-12T17:28:44","guid":{"rendered":"http:\/\/rankexploits.com\/musings\/?p=16472"},"modified":"2011-08-12T15:22:40","modified_gmt":"2011-08-12T21:22:40","slug":"weighted-prediction-of-ice-melt","status":"publish","type":"post","link":"https:\/\/rankexploits.com\/musings\/2011\/weighted-prediction-of-ice-melt\/","title":{"rendered":"Weighted Prediction of Ice Melt"},"content":{"rendered":"<p>Wednesday, I posted an update on the probability of a new record for the minimum 7-day average JAXA ice extent and also discussed the possibility of developing a prediction based on weighting a number of candidate predictive models using their corrected Akaike criterion (AICc).  Today, I&#8217;m going to explain how I weight predictions from a range of candidate models.  I think the discussion will also allow readers to see that choice of model can affect the estimated value for ice minimums.  <\/p>\n<p>For those who want the short story though: Today, my estimate that we will break the 7-day average JAXA sea ice extent minimum is 11%.  If we were betting on the minimum today, I would enter 4.63 million km^2.<br \/>\n<!--more--><\/p>\n<p>Recall that in <a href=\"http:\/\/rankexploits.com\/musings\/2011\/nh-ice-update-vulnerable-ice-method\/\">the previous post<\/a> I had decided to attempt to come up with the best predictor of the minimum 7-day average JAXA ice extent using time series for ice extent and ice area and no other information.  In that post, I had said I was limiting my method to <i>single<\/I> regressions. Oddly enough, the main reason for that limitation was that, on Wednesday, none of the multiple regressions achieved statistical significance for all parameters. I thought I could simplify blog discussion by ignoring multiple regressions.<\/p>\n<p> But, yesterday that changed. So, today, I will be including a multiple regression in my  list of candidate models.  The candidate models, which meet the criteria of being based on time series for Ice Extent or Ice Area only and having all fitting parameters other than the constant shift statistically significant are:<\/p>\n<ol>\n<li>Min. 7-day JAXA ice extent v Current 7-day JAXA ice extent.<\/li>\n<li>Min. 7-day JAXA ice extent v Current 7-day CT area.<\/li>\n<li>Remaining melt v Current  &#8220;Vulnerable ice&#8221; area (i.e. difference between Current 7-day JAXA ice extent and  Current 7-day CT area ).<\/li>\n<li>Min. 7-day JAXA ice extent v Current 7-day CT area <i>and<\/i> Current  &#8220;Vulnerable ice&#8221; area.<\/li>\n<\/ol>\n<p>My method of selecting candidates and determining the best estimate for the minimum 7-day average sea ice extent was to:<\/p>\n<ul>\n<li>Perform a regression between the regressand  (i.e. item I wish to predict ) against the regressors (i.e. items I hope will predict the regressand).<\/li>\n<li>Excluding the intercept, check whether the coefficients for the fit are statistically significant at the \u00c2\u00b195% level. If all coefficients are signficant, this model is included in the list of candidate models.<\/li>\n<li>Find the corrected Akaike coefficient (AICc) for each candidate model, &#8216;i&#8217;,  $latex AICc_i $ <\/li>\n<li>Find the minimum   $latex AICc_{min} $ of all candidate models. The model with the minimum AICc coefficient is the most probable model based on AICc.<\/li>\n<li>Compute the probability that a model &#8216;i&#8217; is &#8220;best&#8221; relative to the most probable model using $latex p_i=exp(-(AICc_i &#8211; AICc_{min})\/2) $.\n<li>For each candidate model, determine the best estimate for the minimum 7 day ice extent,$latex y_i $ , and the standard error in that estimate, $latex s_i $. To compute standard error, I take the sum of the squares of the residuals, and also include an estimate of the uncertainty arising from the uncertainty in the estimate for the fitting parameters.\n<p>Processing up to this point resulted in the following results for each of the four candidate models:<br \/>\n&nbsp;<\/p>\n<table width=\"90%\">\n<caption>Candidate Models and Predictions for NH Ice Extent Minimum.<\/caption>\n<tr>\n<td ALIGN=\"center\">Regressors<\/td>\n<td ALIGN=\"center\"> AICc<\/td>\n<td ALIGN=\"center\">Prediction<\/td>\n<td ALIGN=\"center\">Standard Error<\/td>\n<td ALIGN=\"center\">$latex p_i $<\/td>\n<\/tr>\n<tr>\n<td td ALIGN=\"center\"> <\/td>\n<td ALIGN=\"center\"> <\/td>\n<td ALIGN=\"center\" col=\"2\">million km^2  <\/td>\n<td ALIGN=\"center\"><\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Extent <\/td>\n<td ALIGN=\"center\">900.1 <\/td>\n<td ALIGN=\"center\">4.80<\/td>\n<td ALIGN=\"center\">.32<\/td>\n<td ALIGN=\"center\">0.014<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Area<\/td>\n<td ALIGN=\"center\">895.2<\/td>\n<td ALIGN=\"center\">4.49 <\/td>\n<td ALIGN=\"center\">.30<\/td>\n<td ALIGN=\"center\">0.161<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Vulnerable Area <\/td>\n<td ALIGN=\"center\">891.6<\/td>\n<td ALIGN=\"center\">4.70 <\/td>\n<td ALIGN=\"center\">.26<\/td>\n<td ALIGN=\"center\">1.000<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Extent and Vulnerable<\/td>\n<td ALIGN=\"center\">891.7<\/td>\n<td ALIGN=\"center\">4.58<\/td>\n<td ALIGN=\"center\">.28<\/td>\n<td ALIGN=\"center\">0.935<\/td>\n<\/tr>\n<\/table>\n<p>(Note: The most probable model is based on prediction of remaining ice loss based on &#8220;vulnerable ice&#8221; discussed on Wednesday.  The prediction of total extent based on both current area and current amount of vulnerable ice is assessed as 93.5% as likely best model. )<\/p>\n<\/li>\n<li>Exclude any candidate model with $latex p_i<0.05 $ from further consideration. Based on this, I removed the model predicting the minimum NH ice extent based on the current extent from my list of candidates. <\/li>\n<li>Compute weights for each model using $latex w_i=p_i\/\\sum(p_i) $ where the sum is computed over the models remaining after the previous step.  These weights are estimates of the probability that the model &#8216;i&#8217; is &#8220;true&#8221; under the assumption that the collection of models considers includes the true model. <\/li>\n<li>Because each model &#8216;i&#8217; is assumed to a probability $latex w_i $ of being &#8220;true&#8221;, the best estimate based on information from all models $latex y_{weighted}= \\sum{( w_i y_i)}.$<\/li>\n<li>Estimate of the uncertainty requires first determining the bias between my stated best estimate, $latex y_{weighted} $ and the true value if a particular model is true, $latex b_i=y_i- y_{weighted}   $.  The conditional rms of the errors if model &#8216;i&#8217; is true is then $latex  sc_i=\\sqrt{(s_i^2 + b_i^2 )}$.  The uncertainty for the weighted model is $latex s_{weighted}= \\sqrt{\\sum( w_i sc_i^2)}.$<br \/>\n&nbsp;<\/p>\n<table width=\"90%\">\n<caption>Best Estimate based on Weighting of Candidate Models<\/caption>\n<tr>\n<td ALIGN=\"center\">Regressors<\/td>\n<td ALIGN=\"center\"> AICc<\/td>\n<td ALIGN=\"center\">Prediction<\/td>\n<td ALIGN=\"center\">Standard Error<\/td>\n<td ALIGN=\"center\">$latex w_i $<\/td>\n<\/tr>\n<tr>\n<td td ALIGN=\"center\"> <\/td>\n<td ALIGN=\"center\"> <\/td>\n<td ALIGN=\"center\" col=\"2\">million km^2  <\/td>\n<td ALIGN=\"center\"><\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Area<\/td>\n<td ALIGN=\"center\">895.2<\/td>\n<td ALIGN=\"center\">4.49 <\/td>\n<td ALIGN=\"center\">.30<\/td>\n<td ALIGN=\"center\">0.077<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Vulnerable Area <\/td>\n<td ALIGN=\"center\">891.6<\/td>\n<td ALIGN=\"center\">4.70 <\/td>\n<td ALIGN=\"center\">.26<\/td>\n<td ALIGN=\"center\">0.477<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Extent and Vulnerable<\/td>\n<td ALIGN=\"center\">891.7<\/td>\n<td ALIGN=\"center\">4.58<\/td>\n<td ALIGN=\"center\">.28<\/td>\n<td ALIGN=\"center\">0.446<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\" col=\"4\">Final model<\/td>\n<\/tr>\n<tr>\n<td ALIGN=\"center\">Weighted<\/td>\n<td ALIGN=\"center\">&#8212;<\/td>\n<td ALIGN=\"center\">4.63<\/td>\n<td ALIGN=\"center\">.29<\/td>\n<td ALIGN=\"center\">&#8212;<\/td>\n<\/tr>\n<\/table>\n<\/li>\n<\/ul>\n<p>On Wednesday, I posted a graph illustrating the prediction of the remaining ice extent loss based on the amount of vulnerable ice. Today, I&#8217;ll post the best fit prediction of the minimum 7-day ice extent based on current 7 day average ice area (blue) along with the best fit prediction based on that model (orange circles):<\/p>\n<p><a href=\"http:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2011\/08\/WeightedPrediction.png\"><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2011\/08\/WeightedPrediction-500x500.png\" alt=\"\" title=\"WeightedPrediction\" width=\"500\" height=\"500\" class=\"aligncenter size-medium wp-image-16474\" srcset=\"https:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2011\/08\/WeightedPrediction-500x500.png 500w, https:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2011\/08\/WeightedPrediction-300x300.png 300w, https:\/\/rankexploits.com\/musings\/wp-content\/uploads\/2011\/08\/WeightedPrediction.png 1008w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><br \/>\nThe orange caption indicates that if the model based on current ice <i>area<\/i> is true, the best estimate of the minimum 7 day ice extent is 4.49 million km^2 and there is a 24% probability of breaking the 2007 ice extent minimum.  This is the highest probability estimate based on candidate models used to create the weighted model. <\/p>\n<p>The best fit curve ice extent based on the probability weighted model is 4.63 million km^2 and illustrated with slate-blue triangles.  Note predicted minimum based on the weighted model exceeds that based on the ice area, and using the weighted model the probability that the 2007 minimum will be broken is estimated as 11%.  <\/p>\n<p>Also note the predicted minimum exceeds my Wed estimate that the minimum ice extent of 4.7 million km^3 and the probability of a minimum would be 9%.  Because new data is available everyday, my estimates will change each day. However, the main reason for the change today is that I added a candidate model when the multiple regression based on both area and vulnerable ice achieved significance.  This model predicts a lower extent than the method based on vulnerable ice alone and is assessed as nearly as probable as the model based on vulnerable ice alone. The consequence is a reduced estimate for minimum extent.<\/p>\n<p><b>How are blackboard bettors doing?<\/b><br \/>\nI added purple traces to the graph to describe how Blackboard bettors are doing in purple. The solid purple line passing through the black circle shows the average of blackboard bets excluding Shoosh\/Jay&#8217;s ridiculous bet.  The upper dashed line denotes the top 95% percentile cutoff. The lower dashed line denotes the lower 95% percentile of bettors.  <\/p>\n<p>The mean blackboard bettor placed a bet approximately equal to last years ice extent minimum. Comparison of the upper line indicates that a few blackboard bettors have placed bets that currently fall well outside my \u00c2\u00b195% uncertainty intervals based on the weighted model.  Interestingly enough the lower 95% uncertainty intervals for the weighted model happens to exactly match the lower 95% cut-off for bets.   <\/p>\n<p>Going forward, I will be using this general method to provide best estimate of the ice minimum. As we get nearer and nearer the minimum and use fresh data, we should expect the uncertainty intervals to collapse down to zero.   I&#8217;ll also be showing where my current estimate for the upcoming minimum falls relative to the bets placed and since a few people placed very high bets, we can probably start reporting who is now out of contention on this bet.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wednesday, I posted an update on the probability of a new record for the minimum 7-day average JAXA ice extent and also discussed the possibility of developing a prediction based on weighting a number of candidate predictive models using their corrected Akaike criterion (AICc). Today, I&#8217;m going to explain how I weight predictions from a &hellip; <a href=\"https:\/\/rankexploits.com\/musings\/2011\/weighted-prediction-of-ice-melt\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Weighted Prediction of Ice Melt<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[334],"class_list":["post-16472","post","type-post","status-publish","format-standard","hentry","category-betting","tag-nh-ice"],"_links":{"self":[{"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/posts\/16472","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/comments?post=16472"}],"version-history":[{"count":0,"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/posts\/16472\/revisions"}],"wp:attachment":[{"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/media?parent=16472"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/categories?post=16472"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rankexploits.com\/musings\/wp-json\/wp\/v2\/tags?post=16472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}