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Survey: What’s new in forecasting software?

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Survey: What’s new in forecasting software? / Fildes, Robert; Schaer, Oliver; Svetunkov, Ivan; Yusupova, Alisa.

In: Operations Research Management Science Today, Vol. 47, No. 4, 04.08.2020.

Research output: Contribution to Journal/MagazineJournal article

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Fildes, Robert ; Schaer, Oliver ; Svetunkov, Ivan ; Yusupova, Alisa. / Survey: What’s new in forecasting software?. In: Operations Research Management Science Today. 2020 ; Vol. 47, No. 4.

Bibtex

@article{ed11c55fa66d48e5bcd9b5d2495253dd,
title = "Survey: What{\textquoteright}s new in forecasting software?",
abstract = "When a company is looking into purchasing forecasting software, it inevitably faces the kid-in-a-candy shop situation: There are lots of options, but the forecasting needs may well be quite specific, and the budget is typically constrained. Never has there been a larger choice of forecasting software solutions on the market than now. In addition to new startups that supply very specific machine learning (ML) and artificial intelligence (AI) algorithms, established business intelligence software companies are adding more and more predictive time-series analysis tools to their product lines. As a result, it is difficult to choose the most appropriate product to meet prospective users{\textquoteright} needs. With this years{\textquoteright} biennial forecasting software survey, we aim to provide some insights on the recent development in this industry sector.",
author = "Robert Fildes and Oliver Schaer and Ivan Svetunkov and Alisa Yusupova",
year = "2020",
month = aug,
day = "4",
doi = "10.1287/orms.2020.04.05",
language = "English",
volume = "47",
journal = "Operations Research Management Science Today",
issn = "1085-1038",
number = "4",

}

RIS

TY - JOUR

T1 - Survey: What’s new in forecasting software?

AU - Fildes, Robert

AU - Schaer, Oliver

AU - Svetunkov, Ivan

AU - Yusupova, Alisa

PY - 2020/8/4

Y1 - 2020/8/4

N2 - When a company is looking into purchasing forecasting software, it inevitably faces the kid-in-a-candy shop situation: There are lots of options, but the forecasting needs may well be quite specific, and the budget is typically constrained. Never has there been a larger choice of forecasting software solutions on the market than now. In addition to new startups that supply very specific machine learning (ML) and artificial intelligence (AI) algorithms, established business intelligence software companies are adding more and more predictive time-series analysis tools to their product lines. As a result, it is difficult to choose the most appropriate product to meet prospective users’ needs. With this years’ biennial forecasting software survey, we aim to provide some insights on the recent development in this industry sector.

AB - When a company is looking into purchasing forecasting software, it inevitably faces the kid-in-a-candy shop situation: There are lots of options, but the forecasting needs may well be quite specific, and the budget is typically constrained. Never has there been a larger choice of forecasting software solutions on the market than now. In addition to new startups that supply very specific machine learning (ML) and artificial intelligence (AI) algorithms, established business intelligence software companies are adding more and more predictive time-series analysis tools to their product lines. As a result, it is difficult to choose the most appropriate product to meet prospective users’ needs. With this years’ biennial forecasting software survey, we aim to provide some insights on the recent development in this industry sector.

U2 - 10.1287/orms.2020.04.05

DO - 10.1287/orms.2020.04.05

M3 - Journal article

VL - 47

JO - Operations Research Management Science Today

JF - Operations Research Management Science Today

SN - 1085-1038

IS - 4

ER -