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The Makridakis Competitions (also known as the M Competitions or M-Competitions) are a series of open competitions to evaluate and compare the accuracy of different time series forecasting methods. They are organized by teams led by forecasting researcher Spyros Makridakis and were first held in 1982. [1] [2] [3] [4]
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Anthony John Goldbloom (born 21 June 1983) is the founder and former CEO of Kaggle, a data science competition platform which has used predictive modelling competitions to solve data problems for companies, such as NASA, Wikipedia, [1] Ford and Deloitte.
A financial forecast is an estimate of future financial outcomes for a company or project, usually applied in budgeting, capital budgeting and / or valuation. Depending on context, the term may also refer to listed company (quarterly) earnings guidance. For a country or economy, see Economic forecast.
citation analysis 2012 [81] [82] F. Galgani et al. Blogger Authorship Corpus Blog entries of 19,320 people from blogger.com. Blogger self-provided gender, age, industry, and astrological sign. 681,288 Text Sentiment analysis, summarization, classification 2006 [83] [84] J. Schler et al. Social Structure of Facebook Networks
Resources, events, agents (REA) is a model of how an accounting system can be re-engineered for the computer age.REA was originally proposed in 1982 by William E. McCarthy as a generalized accounting model, [1] and contained the concepts of resources, events and agents (McCarthy 1982).
The best estimate of the state of the system based on the correction to the forecast determined by a weighting factor times the innovation is called the analysis. In one dimension, computing the analysis could be as simple as forming a weighted average of a forecasted and observed value.
DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines. [3] [4] [5] DVC works on top of Git repositories [6] and cloud storage. [7]