Sentence examples for prices to predict from inspiring English sources

That is, one can simply use the standard Leontief model in current prices to predict the sectoral gross outputs (and/or any other factor of interest) as required for an exogenously specified final demand in current prices.

Farecast uses millions of observations of past airfare prices to predict whether a ticket price between two cities for a particular date is expected to decline or increase in price over the next seven days.

The airfare prediction Farecast launches its public beta today, enabling travelers to leverage more than 60 billion records of past airfare prices to predict whether prices will rise or fall over the next 7 days.

Pit.ai founder Yves-Laurent Kom Samo explained to TechCrunch in an interview that most of these funds have one thing in common — they're using information that hasn't been factored into stock prices to predict returns.

Pit.ai founder Yves-Laurent Kom Samo explained to TechCrunch in an interview that most of these funds have one thing in common — they're using information that hasn't been factored into stock prices to predict returns.

"One would like to predict prices, to predict economic development," allows Mandelbrot, an imposing man with the attitude and aspect of a polar bear, who splits his time between research labs at IBM and Yale University.

Recommendation of bundles of products should also involve personalized pricing to predict which price should be offered to a user in order for the bundle to maximize purchase probability.

The Hopper app analyzes billions of flight prices daily to predict how prices will change and tell you when to buy your tickets.

Its forecasting software tracks airlines' ever-changing ticket prices in order to predict prices and alert you when it's the best time to buy a ticket.

Its forecasting software tracks airlines' ever-changing ticket prices in order to predict prices and alert you when it's the best time to buy a ticket.

In this project, the researchers will use household survey data to train a machine learning model which will consider SIF, land-surface temperature and food-price information to predict where extreme poverty and undernutrition are likely to strike.