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  2. Makridakis Competitions - Wikipedia

    en.wikipedia.org/wiki/Makridakis_Competitions

    The purpose of the M2-Competition was to simulate real-world forecasting better in the following respects: [6] Allow forecasters to combine their trend-based forecasting method with personal judgment. Allow forecasters to ask additional questions requesting data from the companies involved in order to make better forecasts.

  3. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    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.

  4. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...

  5. Tracking signal - Wikipedia

    en.wikipedia.org/wiki/Tracking_signal

    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.

  6. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Many businesses have to account for risk exposure due to their different services and determine the costs needed to cover the risk. Predictive analytics can help underwrite these quantities by predicting the chances of illness, default , bankruptcy , etc. Predictive analytics can streamline the process of customer acquisition by predicting the ...

  7. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    A person can become better calibrated [citation needed] — i.e. having things they give 10% credence to happening 10% of the time. Or they can forecast things more confidently [citation needed] — coming to the same conclusion but earlier. Some have claimed that forecasting is a transferable skill with benefits to other areas of discussion ...

  8. Weather forecasting - Wikipedia

    en.wikipedia.org/wiki/Weather_forecasting

    The forecasting of the weather for the following six hours is often referred to as nowcasting. [70] In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model.

  9. determining the order of differencing to make a time series stationary may be an iterative, exploratory process. Compute plain ARMA terms via the usual methods to fit to this stationary temporary data set which is in ersatz units. Forecast either to existing data (static forecast) or "ahead" (dynamic forecast, forward in time) with these ARMA ...