MIT2_854F10_forecast

# MIT2_854F10_forecast - Forecasting Lecturer Prof Duane S...

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1 Forecasting Lecturer: Prof. Duane S. Boning Rev 8

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2 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 the independent variable) Time dependencies Explicit Implicit
3 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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4 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 0 10 20 30 40 50 -10 -5 0 5 time x autocorrelated autocorrelation in the data 0 10 20 30 40 50 -2 0 2 4 time uncorrelated
5 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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## This note was uploaded on 02/24/2012 for the course MECHANICAL 2.854 taught by Professor Stanleygershwin during the Fall '10 term at MIT.

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MIT2_854F10_forecast - Forecasting Lecturer Prof Duane S...

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