Six Sigma Forum June Webinar - Introduction to Time Series Analysis
Join John Noguera, one of our expert members, for our next webinar on June 18 at noon EST. To register, click HERE.
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ABOUT THE SPEAKER:

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).

ABSTRACT:
Time Series Forecasting is an important tool for the Six Sigma or Quality practitioner. Applications include forecasting sales and customer demand, as well modeling a process mean in order to be able to implement SPC for autocorrelated data. The challenge for the practitioner 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. Fortunately recent developments in automated time series forecasting simplify the model selection process. This webinar will introduce time series forecasting and demonstrate these automated tools with SigmaXL software. Similar results can be obtained using the forecast package in R.



WEBINAR AGENDA:
  • Introduction

  • Autocorrelation

  • Example 1: Chemical Process Concentration

  • Simple Exponential Smoothing

  • Information Criteria

  • Forecast Accuracy

  • Example 2: Monthly Airline Passengers

  • Seasonal Trend Decomposition Plots

  • Spectral Density Plots

  • Error, Trend, Seasonal (ETS) Exponential Smoothing models

  • Autoregressive Integrated Moving Average (ARIMA) models

  • Partial Autocorrelation

  • ARIMA with Predictors

  • Example 3: Electricity Demand with Temperature and Work Day Predictors

  • Questions/References