Sentence examples for data-driven forecast from inspiring English sources

In this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results.

Current work is ongoing to extend the approach for longer time scales and to incorporate data-driven forecasting models of RO plant into control strategies and process diagnostics.

In this study, it is aimed to develop a data-driven forecasting tool for tight-gas sands, which are based on artificial neural networks that can complement the physics-driven modeling approach, namely numerical-simulation models.

In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θs) data, was developed using an extreme learning machine (ELM) algorithm.

The data-driven forecasting idea is introduced to address the forecasting difficulties caused by load fluctuation in developed cities.

Two major contributions of this paper are: 1) The data-driven forecasting idea is introduced to address the forecasting difficulties caused by load fluctuation in developed cities.

Therefore, the present contribution presents a benchmark to determine the possibility of training and applying data-driven forecasting models on Big Data.

We also found that only 7% of businesses have "prescriptive analytics" capabilities, such as scenario modeling and data-driven forecasting, with 37% of businesses saying they plan to perform prescriptive analytics within the next two years.

The goals of the benchmark are the assessment of the necessary time to obtain data-driven forecasting models when using Big Data and to determine the point at which a distributed computing framework based on, e.g., Spark becomes necessary.

The benchmark conducted in the present paper has two main goals: (i) to assess the necessary time to obtain data-driven forecasting models on a distributed environment and (ii) to determine the point at which a Big Data computing framework based on Spark becomes necessary.

However, the creation of data-driven forecasting models with Big Data proves to be challenging, since most data-driven approaches have not been designed to work on a distributed environment [8].