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=== Portfolio, product or economy-level prediction ===
Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example, a retailer might be interested in predicting store-level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques (see below). They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power.<ref>{{Cite journal |last=Dhar |first=Vasant |date=May 6, 2011 |title=Prediction in financial markets: The case for small disjuncts |url=https://dl.acm.org/doi/10.1145/1961189.1961191 |journal=ACM Transactions on Intelligent Systems and Technology |language=en |volume=2 |issue=3 |pages=1–22 |doi=10.1145/1961189.1961191 |s2cid=11213278 |issn=2157-6904|url-access=subscription }}</ref><ref>{{Cite journal |last1=Dhar |first1=Vasant |last2=Chou |first2=Dashin |last3=Provost |first3=Foster |date=2000-10-01 |title=Discovering Interesting Patterns for Investment Decision Making with GLOWER ◯-A Genetic Learner Overlaid with Entropy Reduction |journal=Data Mining and Knowledge Discovery |volume=4 |issue=4 |pages=251–280 |doi=10.1023/A:1009848126475 |s2cid=1982544 |issn=1384-5810}}</ref>
=== Underwriting ===
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