Example Time Series & Multivariate Regression in R - Predicting Steel Demand<br />
Time Series <br />The math is pretty substantial (at least for me!)<br />Key concepts are seasonality, auto-regression, tr...
Client management wants to predict demand (in tons) of steel <br />
Some HW Code<br />
Prediction <br />
ARIMA<br />“Seasonal ARIMA modelsare powerful tools in the analysis of time series as they are capable of modeling a very ...
Here’s the code for selecting the best ARIMA parameters<br />
Multivariate Regression<br />Identified about 150 economic indicators; from economy.com and other sources. <br />
1 response; 150 predictors – tedious to find best COR<br />
Now we know top ten predictors for agriculture – let’s build a model<br />
Whack a mole on predictors<br />
Get a nice model<br />
Remembering why we walked into the swamp … oh yea, to predict future tons for agriculture products<br />
Another approach<br />You can “lag” your predictors so that – for example - when you build your model, you associate July ...
Simple code to “lag” <br />R has a<br />Built in<br />Lag function<br />
If you want a copy of slides or code, just email me. <br />Bill Yarberry<br />wayarberry@yahoo.com<br />Thanks.  <br />
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Time series and regression presentation for oct 5th rice presentation r group

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Short presentation on multivariate regression and time series regression using the open source package R

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Time series and regression presentation for oct 5th rice presentation r group

  1. 1. Example Time Series & Multivariate Regression in R - Predicting Steel Demand<br />
  2. 2. Time Series <br />The math is pretty substantial (at least for me!)<br />Key concepts are seasonality, auto-regression, trend and level<br />We used Holt-Winters and ARIMA (auto regression integrated moving average); plenty of other functions exist<br />
  3. 3. Client management wants to predict demand (in tons) of steel <br />
  4. 4. Some HW Code<br />
  5. 5. Prediction <br />
  6. 6. ARIMA<br />“Seasonal ARIMA modelsare powerful tools in the analysis of time series as they are capable of modeling a very wide range of series”<br />Best to learn thoroughly and from deep study. But if you have a day job …. Just pluck code to optimize the parameters and use it<br />
  7. 7. Here’s the code for selecting the best ARIMA parameters<br />
  8. 8. Multivariate Regression<br />Identified about 150 economic indicators; from economy.com and other sources. <br />
  9. 9. 1 response; 150 predictors – tedious to find best COR<br />
  10. 10. Now we know top ten predictors for agriculture – let’s build a model<br />
  11. 11. Whack a mole on predictors<br />
  12. 12. Get a nice model<br />
  13. 13. Remembering why we walked into the swamp … oh yea, to predict future tons for agriculture products<br />
  14. 14. Another approach<br />You can “lag” your predictors so that – for example - when you build your model, you associate July 2011 actual (response) with April of 2011 predictor value. If you have a good model, lagging allows you to predict future values without depending on “experts” to opine on future economic indicators. <br />
  15. 15. Simple code to “lag” <br />R has a<br />Built in<br />Lag function<br />
  16. 16. If you want a copy of slides or code, just email me. <br />Bill Yarberry<br />wayarberry@yahoo.com<br />Thanks. <br />

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