Ensemble Models for Forecasting Microbusiness Density: a Research Study

EasyChair Preprint no. 10920, version 2

Abstract

Microbusinesses play a significant role in the econ- omy, contributing to job creation and economic growth. Accu- rately forecasting microbusiness density is critical for policymak- ers and business owners in making informed decisions. However, forecasting microbusiness density is challenging due to the lack of reliable and comprehensive data. Ensemble models have emerged as a promising approach for forecasting microbusiness density by combining multiple models to improve accuracy. This research study aims to develop and validate ensemble models for forecasting microbusiness density and evaluate their performance using various metrics. The study’s results have significant implications for policymakers and business owners in understanding the factors affecting microbusiness density and making informed decisions to promote economic growth. This pa- per provides an overview of the significance of microbusinesses in the economy, the challenges in forecasting microbusiness density, and how ensemble models can help address these challenges. The methodology for developing ensemble models, data sources, and performance metrics used for evaluating the accuracy of the models are also described. Finally, the paper presents the key findings of the study and their implications for policymakers and business owners.

Keyphrases: Ensemble, machine learning, Microbusiness, neural network

@Booklet{EasyChair:10920,
  author = {Tong Zhou},
  title = {Ensemble Models for Forecasting Microbusiness Density: a Research Study},
  howpublished = {EasyChair Preprint no. 10920},

  year = {EasyChair, 2023}}