Sentence examples for data-driven forecasting from inspiring English sources

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].

However, data-driven forecasting models trained using the available Big Data may be a possible solution.

Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented.

These goals are achieved by comparing the required time for training and applying different data-driven forecasting models on a computing cluster (using Spark) and on a single computer (using R and Spark).

Afterwards, a benchmark comparing the training and evaluation of data-driven forecasting models using different amounts of data, as well as R and Spark on a single computer and Spark on a computing cluster is presented.

In the present paper three different data mining techniques are used to obtain the various data-driven forecasting models: a multiple linear regression (MLR) [32], a least absolute shrinkage and selection operator (LASSO) [32], and a random forest [33].

We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation.

Starting from efficient material use, accurate (data-driven) demand forecasts, eliminating overstock, and refraining from operating offline stores.

In the work, classic data-driven models for load forecasting in buildings are used as an example.

This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow.

2) Data-driven method: construct optimal ARIMA model for each original subsequence in Fig. 2b and forecast each one, then sum up all the forecasting results to obtain the system load forecasting results.