Sentence examples for and forecast for each from inspiring English sources

The phrase "and forecast for each" is correct and usable in written English.
It can be used when discussing predictions or projections related to multiple items or categories.
Example: "We need to analyze the sales data and forecast for each product line to determine our next steps."
Alternatives: "and project for each" or "and estimate for each".

Exact(1)

Powered by Weather Underground, the free app boasts current temp, wind (speed and direction) and forecast for each of the City by the Bay's 17 different microclimates (read: neighborhoods).

Similar(59)

Motivate team members by creating project timelines and forecasts for each phase of development.

Alan Roufa, administrative manager for the East Plant, plans to develop a method for employees to track and forecast costs for each piece of equipment in their departments.

Weatherbug (detailed weather and forecasts for any location).

Our model integrates TTR data with information on multiple tiers of supplier relationships, bill-of-material information, operational and financial measures, in-transit and on-site inventory levels, and demand forecasts for each product.

The GLWLD is a Flash-based tool that can simultaneously display time-series of measured monthly and annual water level data and seasonal forecasts for each of the Great Lakes, reconstructed lake levels from paleoclimate research, and decadal lake level projections under alternative climate scenarios.

The buying committee had traditionally provided a single consensus forecast for each style and color.

We decided to ask each member of the committee to make an independent forecast for each style and color.

This decentralization would contribute to production of more reliable and accurate forecasts for each specific zone.

This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron.

First, we train and generate supervised forecasts for each time series.

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