<p>It looks like I'm one of the more active users in the <a href="https://stats.stackexchange.com/tags/forecasting/topusers">forecasting</a> and <a href="https://stats.stackexchange.com/tags/time-series/topusers">time-series</a> tags. Here is my subjective take.</p>

<p>I very much agree with <a href="https://stats.meta.stackexchange.com/questions/5133/why-do-time-series-questions-get-such-a-low-response-rate?cb=1#comment15167_5133">Nick Cox' comment</a>. Many, if not most of the questions especially in the forecasting tag seem to fall into one of two categories:</p>

<ol>
<li><p>The OP has quite obviously not read anything on forecasting, such as the excellent free online forecasting textbook <a href="http://otexts.org/fpp2/" rel="nofollow noreferrer"><em>Forecasting: Principles and Practice</em> by Hyndman &amp; Athanasopoulos (2nd ed.)</a>, which I keep recommending over and over. And over.</p>

<p>With steadily decreasing enthusiasm, though. By now, anyone who looks through CV before posting, or who performs a simple search (the 1st edition of <em>FPP</em> is <a href="https://duckduckgo.com/?q=forecasting+textbook" rel="nofollow noreferrer">the very first hit if you search for "forecasting textbook" at duckduckgo.com</a> - your results on Google will depend on your personal search bubble) should find it. </p>

<p>If people obviously haven't found <em>FPP</em>, or <em>have</em> found it or a similar forecasting textbook, but have decided to rather post a question than look through the textbook before posting, this seems to be a clear case for downvoting ("shows no research effort"), but considering that these questions often seem to come from new users, slapping them with a downvote on their first question seems to be overly harsh. So I'll often comment that they look at <em>FPP2</em>, instead of writing a full answer.</p></li>
<li><p>The question is a slight variation on one of the many, many questions we already have on forecasting. It's a hassle to find the closest match, and even if I do find one, it's often not obvious to the OP <em>why</em> this duplicate contains the answer to their question - if they knew enough about forecasting to understand the connection between the duplicate and their question, they wouldn't be asking in the first place.</p>

<p>So voting to close as a duplicate will often require some commentary. I'd love to have the time to provide this commentary; unfortunately, my hungry children rely on me to put food on the table, and my employer does not pay me for spending time on CV. So I often write a comment to point to a related question and leave it at that.</p>

<p>Case in point: I recently looked at a question <a href="https://stats.stackexchange.com/q/326193/1352">where the OP essentially asked why the forecast was a flat line</a>. I thought that there should have been tons of answers to this exact question, but I only found <em>similar</em> questions, no duplicates or canonical answers, so this time, I went and wrote a short answer, essentially to have a duplicate to refer to later on, since this kind of question <em>will</em> appear again.</p></li>
</ol>

<p>It doesn't help that forecasting seems to be a topic with a high share of drive-by posters. I have a mental image of someone with little to no statistical knowledge being saddled with the task of forecasting sales, because they didn't look busy when the boss came around, or because they are known to be "good with figures". So off they go with little understanding and post a question along the lines of (1) and (2) above here. And even if they do get an answer, they don't accept or upvote. No, I'm not <em>only</em> here because rep tickles my already-inflated ego - but writing an answer and not getting <em>any</em> feedback doesn't encourage me to write more answers the next time a 1-rep user asks an un-upvoted not-very-good forecasting question. Sorry.</p>

<p>Conversely, my mental image (I emphasize that I'm just conjecturing here, I don't have data) of someone who posts in the mixed-model or chi-squared tags is someone who has at least looked into a statistics textbook or taken a stats course at some point in time, maybe while studying psychology or medicine. Answering here is typically more gratifying.</p>

<p>A related problem is that there is no clear point where a forecasting problem is "answered". Here, I am thinking of the time <em>before</em> the question is asked at CV. Someone may have read about Exponential Smoothing or ARIMA and applied it to their data, but they may feel that forecasts could be better. And this is often the point where they come and post questions on more elaborate methods here. In forecasting, it's not clear when the search ends. (Compare many other tags here.) This is exacerbated by my point above, of forecasters typically not having a lot of statistical training, so disabusing them of the notion that adding more data or using more complex models will <em>not</em> automatically improve forecast accuracy is an uphill battle.</p>

<p>So, to answer your question of whether anything can be done to improve the answer rate on forecasting and time-series questions: unless questions improve in quality (unlikely), it seems like we may need to be less stringent in closing as duplicates, or we might want to tolerate (and write) more short answers. Of course, it would help if we got more people to answer in these tags, too, so I'm looking forward to upvoting many answers from you in the future ;-)</p>
