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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.
John Galt Solutions is a privately held software company that provides forecasting and supply chain planning for mid-market companies. [1] [2]Founded in 1996 and headquartered in Chicago, they claim more than 6,000 customers worldwide use John Galt Solutions products every day.
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).
There have also been proposed methods for adjusting the smoothing constants used in forecasting methods based on some measure of prior performance of the forecasting model. One such approach is suggested by Trigg and Leach (1967), which requires the calculation of the tracking signal.
Dialogue Systems Research 2015 [73] Lowe, R. et al. Dialog State Tracking Challenge The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. Transcription of spoken dialogs with labelling
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.
Data and model versioning is the base layer [21] of DVC for large files, datasets, and machine learning models. It allows the use of a standard Git workflow, but without the need to store those files in the repository. Large files, directories and ML models are replaced with small metafiles, which in turn point to the original data.