O'Reilly on Data

JANUARY 22, 2019

Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below. For such distributions, parameter values based on historical data are bound to introduce errors into forecasts. Not even close. References. Thompson, L.S.

Modeling 134

Modeling Finance Uncertainty Data mining 134

bridgei2i

JULY 28, 2020

She’s the founder and CEO of StatWeather, a company, which was recognized as number one in climate technology globally in the year, 2017, by the Energy Risk Awards. In my company StatWeather we use this kind of data and data mining to forecast weather and climate patterns, which has been very successful.

Enterprise 98

Enterprise Digital Transformation Insurance B2B 98

Domino Data Lab

MAY 20, 2021

note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Data mining for direct marketing: Problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. link] Fisher, R.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

datapine

NOVEMBER 27, 2019

You simply choose the data source you want to analyze and the column/variable (for instance, revenue) that the algorithm should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and what-if analysis. BN by 2023, with a CAGR of 13.6%

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361