Time series and regression presentation for oct 5th rice presentation r group
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The document discusses predicting steel demand using time series and multivariate regression techniques in R, emphasizing key concepts like seasonality and auto-regression. It highlights the use of ARIMA models and the identification of significant economic indicators for building accurate predictive models. The author also offers code samples and encourages further learning for better parameter optimization.
Time series and regression presentation for oct 5th rice presentation r group
- 1. Example Time Series & Multivariate Regression in R - Predicting Steel Demand
- 2. Time Series The math is pretty substantial (at least for me!)Key concepts are seasonality, auto-regression, trend and levelWe used Holt-Winters and ARIMA (auto regression integrated moving average); plenty of other functions exist
- 3. Client management wants to predict demand (in tons) of steel
- 4. Some HW Code
- 5. Prediction
- 6. ARIMA“Seasonal ARIMA modelsare powerful tools in the analysis of time series as they are capable of modeling a very wide range of series”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
- 7. Here’s the code for selecting the best ARIMA parameters
- 8. Multivariate RegressionIdentified about 150 economic indicators; from economy.com and other sources.
- 9. 1 response; 150 predictors – tedious to find best COR
- 10. Now we know top ten predictors for agriculture – let’s build a model
- 11. Whack a mole on predictors
- 12. Get a nice model
- 13. Remembering why we walked into the swamp … oh yea, to predict future tons for agriculture products
- 14. Another approachYou 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.
- 15. Simple code to “lag” R has aBuilt inLag function
- 16. If you want a copy of slides or code, just email me. Bill Yarberrywayarberry@yahoo.comThanks.