CMAF FFT: SPC for Autocorrelated Data Using Automated Time Series Forecasting скачать в хорошем качестве

CMAF FFT: SPC for Autocorrelated Data Using Automated Time Series Forecasting

This is the eighth webinar in the series of "Friday Forecasting Talks", hosted by Centre for Marketing Analytics and Forecasting of Lancaster University, UK. Follow us on LinkedIn:   / lancastercmaf   CMAF on Twitter:   / lancastercmaf   CMAF webinars: https://cmaf-fft.lp151.com/ - Contents of this video ------------------------- 00:00 - Introduction 02:02 - Statistical Process Control 09:42 - SPC for autocorrelated data 14:10 - SigmaXL demonstration 25:10 - Seasonal time series example 33:12 - Q&A session The abstract: Statistical process control for autocorrelated processes have been addressed using the EWMA (Exponentially Weighted Moving Average) one-step-ahead forecast or simple ARIMA (Auto-Regressive Integrated Moving Average) models. The time series model forecasts the motion in the mean and an Individuals control chart is plotted of the residuals to detect assignable causes. Failure to account for the autocorrelation will produce limits that are too narrow resulting in excessive false alarms, or limits that are too wide resulting in misses. The challenge with this approach is that if there is seasonality or negative autocorrelation in the data, the user needs an advanced level of knowledge in forecasting methods to pick the correct model. In this session, we will review simple exponential smoothing / EWMA and then introduce recent developments in time series forecasting that use automatic model selection to accurately pick the time series model that produces a minimum forecast error. Bio: John Noguera is Co-founder and Chief Technology Officer of SigmaXL, Inc., a leading provider of user-friendly Excel add-ins for Lean Six Sigma tools, statistical & graphical analysis and Monte Carlo simulation. He leads the development of SigmaXL and DiscoverSim with a passion for ease-of-use, practical & powerful features, and statistical accuracy. John is a certified Six Sigma master black belt and was an instructor at Motorola University. He has authored conference papers on Statistical Process Control and Six-Sigma Quality and is a contributing author in the Encyclopedia of Statistics in Quality and Reliability (Wiley). SigmaXL website: https://www.sigmaxl.com/