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  2. Makridakis Competitions - Wikipedia

    en.wikipedia.org/wiki/Makridakis_Competitions

    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]

  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. Data Version Control (software) - Wikipedia

    en.wikipedia.org/wiki/Data_Version_Control...

    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.

  5. Jeremy Howard (entrepreneur) - Wikipedia

    en.wikipedia.org/wiki/Jeremy_Howard_(entrepreneur)

    Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator. [1]He is the co-founder of fast.ai, where he teaches introductory courses, [2] develops software, and conducts research in the area of deep learning.

  6. Forecasting - Wikipedia

    en.wikipedia.org/wiki/Forecasting

    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.

  7. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    Extension packages contain related and extended functionality: package tseries includes the function arma(), documented in "Fit ARMA Models to Time Series"; packagefracdiff contains fracdiff() for fractionally integrated ARMA processes; and package forecast includes auto.arima for selecting a parsimonious set of p, q.

  8. Forecast either to existing data (static forecast) or "ahead" (dynamic forecast, forward in time) with these ARMA terms. Apply the reverse filter operation (fractional integration to the same level d as in step 1) to the forecasted series, to return the forecast to the original problem units (e.g. turn the ersatz units back into Price).

  9. Mean absolute scaled error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_scaled_error

    Download QR code; Print/export Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... can be used as the "base" forecast. [10]