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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.
The Global Energy Forecasting Competition (GEFCom) is a competition conducted by a team led by Dr. Tao Hong that invites submissions around the world for forecasting energy demand. [1] GEFCom was first held in 2012 on Kaggle , [ 2 ] and the second GEFCom was held in 2014 on CrowdANALYTIX.
Cash flow forecasting is the process of obtaining an estimate of a company's future cash levels, and its financial position more generally. [1] A cash flow forecast is a key financial management tool, both for large corporates, and for smaller entrepreneurial businesses. The forecast is typically based on anticipated payments and receivables.
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.
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.
The original model uses an iterative three-stage modeling approach: Model identification and model selection: making sure that the variables are stationary, identifying seasonality in the dependent series (seasonally differencing it if necessary), and using plots of the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the dependent time series to decide which (if any ...
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.