"forecasting strategy" is a correct and usable phrase in written English.
You can use it when discussing different strategies that a company may use to predict its future performance. For example, "The company's forecasting strategy was very effective and helped them to make accurate predictions for the following quarter."
In this paper, a new forecasting strategy for statistical wind power ramp events is proposed.
The proposed forecasting strategy produces the lowest errors across all times to maturity.
Therefore, the proposed multi-stage hierarchical forecasting strategy is both novel and effective for short-term wind power forecasting.
Hence, a particular forecasting strategy is employed by a workflow system and applied uniformly to all its workflow activities.
In this paper, a new wind power ramp event forecasting strategy is proposed and its efficiency is demonstrated by using several numerical experiments of an actual wind plant.
Accordingly, we propose a forecasting strategy that involves switching between a model of interest and the benchmark model, based on observations of their recent past performance.
Case studies on successful (or unsuccessful) demand forecasting strategies in different segments of the apparel market, such as fast fashion, luxury, or mass market, could also be interesting.
First, the signal filtering technique (SSA) is applied for data pre-processing and the novel model subsequently models the resultant series with different forecasting strategies using SVM optimized by the CS algorithm.
Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme.
The experimental results on real world examples and simulated test cases demonstrate the excellent performance of our strategy in the forecasting of activity duration intervals for both long-duration and short-duration activities in comparison to some representative time-series forecasting strategies in traditional workflow systems.
Due to such a problem, the forecasting strategies in workflow systems must adapt to different workflow applications which are normally executed repeatedly such as data/computation intensive scientific applications (mainly with long-duration activities) and instance intensive business applications (mainly with short-duration activities).
