sma 6304 / mit 2.853 / mit 2.854 manufacturing systems lecture 11: forecasting lecturer: prof. duane...
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SMA 6304 / MIT 2.853 / MIT 2.854Manufacturing Systems
Lecture 11: Forecasting
Lecturer: Prof. Duane S. Boning
Copyright 2003 © Duane S. Boning. 1
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Agenda1.Regression • Polynomial regression • Example (using Excel)
2.Time Series Data & Regression • Autocorrelation –ACF • Example: white noise sequences • Example: autoregressive sequences • Example: moving average • ARIMA modeling and regression
3.Forecasting Examples
Copyright 2003 © Duane S. Boning. 2
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Regression –Review&Extensions
•Single Model Coefficient: Linear Dependence
•Slope and Intercept (or Offset):
•Polynomial and Higher Order Models:
•Multiple Parameters
•Key point: “linear” regression can be used as long as the model is linear in the coefficients (doesn’t matter the dependence in theindependent variable)
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Polynomial Regression Example•
• Replicate data provides opportunity to check for lack of fit
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Growth Rate –First Order Model
• Mean significant, but linear term not
• Clear evidence of lack of fit
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Growth Rate –Second Order Model
• No evidence of lack of fit
• Quadratic term significant
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Polynomial Regression In Excel
• Create additional input columns for each input
• Use “Data Analysis” and “Regression” tool
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Polynomial Regression
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Agenda 1.Regression
• Polynomial regression
• Example (using Excel)
2.Time Series Data & Time Series Regression
• Autocorrelation –ACF
• Example: white noise sequences
• Example: autoregressive sequences
• Example: moving average
• ARIMA modeling and regression
3.Forecasting Examples
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Time Series –Time as an Implicit Parameter
• Data is often collected with a time-order•
• An underlying dynamic process (e.g. due to physics of a manufacturing process) may create autocorrelation in the data
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Intuition: Where Does Autocorrelation Come From?
• Consider a chamber with volume V, and with gas flow in and gas flow out at rate f. We are interested in the concentration x at the output, in relation to a known input concentration w.
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Key Tool: Autocorrelation Function (ACF)
•Time series data: time index i
• CCF: cross-correlation function
• ACF: auto-correlation function
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Stationary vs. Non-Stationary
Stationary series:
Process has a fixed mean
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White Noise –An Uncorrelated Series• Data drawn from IID gaussian
• ACF: We also plot the 3σlimits –values within these not significant
• Note that r(0) = 1 always (a signal is always equal to itself with zero lag –perfectly autocorrelated at k = 0)
• Sample mean
• Sample variance
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Autoregressive Disturbances •Generated by:
•Mean
•Variance
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Another Autoregressive Series
•Generated by:
High negativeautocorrelation
Slow drop in ACF with large αBut now ACF alternates in sign•
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Random Walk Disturbances
•Generated by:
•Mean
•Variance
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Moving Average Sequence •Generated by:
•Mean
•Variance
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ARMA Sequence
•Generated by:
•Both AR & MA behavior
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ARIMA Sequence
•Start with ARMA sequence
•Add Integrated (I) behavior
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Periodic Signal with Autoregressive NoiseOriginal Signal After Differencing
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Agenda
1. Regression
• Polynomial regression
• Example (using Excel)
2. Time Series Data & Regression
• Autocorrelation –ACF
• Example: white noise sequences
• Example: autoregressive sequences
• Example: moving average
• ARIMA modeling and regression
3. Forecasting Examples
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Cross-Correlation: A Leading Indicator •Now we have two series:
–An “input” or explanatory
variable x
–An “output” variable y
•CCF indicates both AR and lag:
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Regression & Time Series Modeling
• The ACF or CCF are helpful tools in selecting an appropriate model structure
– Autoregressive terms?
• xi= αxi-1
– Lag terms?
• yi= γxi-k
• One can structure data and perform regressions
– Estimate model coefficientvalues, significance, and confidence intervals
– Determine confidence intervals on output
– Check residuals
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Statistical Modeling Summary
1. Statistical Fundamentals
• Sampling distributions
• Point and interval estimation
• Hypothesis testing
2. Regression
• ANOVA
• Nominal data: modeling of treatment effects (mean differences)
• Continuous data: least square regression
3. Time Series Data & Forecasting
• Autoregressive, moving average, and integrative behavior
• Auto-and Cross-correlation functions
• Regression and time-series modeling