• NumXL shopping Cart
  •  
    NumXL for Microsoft Excel makes sense of time series analysis:

    • Build, validate, rank models, and forecast right in Excel
    • Keep the data, analysis and models linked together
    • Make and track changes instantly
    • Share your results by sending just one file

    >>Read More


    | Free Trial
  • Home
  • Products
  • Tips & Demos
  • Support
    • Documentation
    • Blog
    • FAQ
    • Library
    • Service Level Agreement
    • Thank you
    • Beta Program
    • Resources
  • About Us
  • Prices
  •  
     

    Have a Question?

    Phone: +1 (888) 427-9486
    +1 (312) 257-3777
    Contact Us

    Home >> Support >> Documentation >> NumXL >> Reference Manual >> Smoothing

    Smoothing

    Smoothing Function playlist tutorial videos

    The term "smoothing" is often used to refer to techniques that can be applied to time series data in order to produce smoothed (less noisy or slower moving) data for presentation, or to make out-of-sample forecasts.

    The techniques supported here range from a simple weighted-moving average (WMAi) to exponential smoothing algorithms. The WMA averages the fixed number of past observations with fixed weights, while the exponential smoothing assigns exponentially decreasing weights over time, including all past observations.

    Smoothing functions

    The smoothing techniques vary in their complexity, based on how they handle trend and seasonality in the time series:

    1. Weighted-moving average (WMA) and Holt's simple exponential smoothing assume a stationary time series
    2. Holt-Winters double exponential smoothing and Holt's linear exponential smoothing are ideal for time series that possess a trend
    3. Winters's triple exponential smoothing takes into account seasonal changes as well as trends.

    Examples

    notes

    1. Exponential smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements.

    References

    • Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
    • Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740

    Related Links

    • Wikipedia - Exponential Smoothing
    • WMA
    • NxTrend
    • SESMTH
    • LESMTH
    • DESMTH
    • TESMTH
    • GESMTH
    ‹ RMNAupWMA ›

    Tips & Hints

    • Keep up with the trends
    • Smoothing

    • Download Sites - NumXL
    Try our full-featured product free for 14 days

    Download NumXL Free 30-day Trial

    Help desk

    Questions?
    Request a feature?
    Report an issue?

    » Go to your help desk «

    Or email us:
    support@numxl.com

    • NumXL 1.65 (HAMMOCK) Está Aquí!
      05/18/2017 - 21:31
    • NumXL 1.65 (HAMMOCK) is Here!
      05/18/2017 - 21:14
    • NumXL 1.64 (TURRET) is here
      12/25/2016 - 13:12

    ARIMA ARMA Forecast Getting Started goodness of fit LLF SARIMA scenario simulation statistical test tutorial user's guide
    more tags
     

    • Support
    • FAQ
    • Demos & Tutorials
    • Documentation
    • Help Desk

    • Resources
    • Order Help

    • About Spider
    • Contact Spider
    • Corporate Information
    • Legal Information
    • Partners

    Follow Us

    Contact | Glossary | Sitemap | Blog | Links
    © 2008-2019 Spider Financial | Disclaimer | Terms of Use | Privacy Policy | Trademarks & Copyrights