<p>Yes, we need both. I think forecasting is more specifically about time-series data -- "What happens next?" Prediction the task of deploying a model to assess unseen data in some way. With prediction, there need not be any time-series component at all; you might just be interested in how well a model does against a holdout set of observations.</p>

<p>Support of this can be found in Cressie &amp; Wikle <em>Statistics for Spatio-Temporal Data</em>, p. 17:</p>

<blockquote>
  <p>Uncertainty in data, processes or parameters means that there will be uncertainty in conclusions. Statisticians call this drawing of conclusions in the presence of uncertainty, <em>statistical inference</em> (or just <em>inference</em>); in this book, inferences will be either <em>estimation</em> of fixed but unknown parameters, or <em>prediction</em> of unknown random quantities. (Notice that "forecasting," namely concluding something about the future, is a special case of "prediction.")</p>
</blockquote>

<p>I've edited this description into the <a href="https://stats.stackexchange.com/questions/tagged/forecasting" class="post-tag" title="show questions tagged &#39;forecasting&#39;" rel="tag">forecasting</a> and <a href="https://stats.stackexchange.com/questions/tagged/prediction" class="post-tag" title="show questions tagged &#39;prediction&#39;" rel="tag">prediction</a> wikis.</p>
