How hard is it to forecast commodity prices?

As previously mentioned, making money in commodities is not easy. To see a difficulty in predicting a commodity, you will forecast the price of chicken to five years in the future. When you complete your forecasts, you will note that even just a few years out, the acceptable range of prices is very large. This is because commodities are subject to many sources of variation.

Recall that you previously fit an SARIMA(2,1,0, 1,0,0)₁₂ model to the monthly US chicken price series chicken. You will use this model to calculate your forecasts.

The astsa package is preloaded for you and the monthly price of chicken data (chicken) are plotted.

Este ejercicio forma parte del curso

ARIMA Models in R

Instrucciones del ejercicio

Refit the SARIMA model from the earlier exercise and convince yourself that it fits well. Check parameter significance and residual diagnostics.
Use sarima.for() to forecast the data 5 years into the future.

Ejercicio interactivo práctico

Prueba este ejercicio completando el código de muestra.

# Fit the chicken model again and check diagnostics


# Forecast the chicken data 5 years into the future

Editar y ejecutar código

Este ejercicio forma parte del curso

ARIMA Models in R

IntermedioNivel de habilidad

4.9+

Comienza el curso gratis

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Exercise 1: First things first Exercise 2: Data play Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

Exercise 1: AR and MA models Exercise 2: Fitting an AR(1) model Exercise 3: Fitting an AR(2) model Exercise 4: Fitting an MA(1) model Exercise 5: AR and MA together Exercise 6: Fitting an ARMA model Exercise 7: Identify an ARMA model Exercise 8: Model choice and residual analysis Exercise 9: Model choice - I Exercise 10: Model choice - II Exercise 11: Residual analysis - I Exercise 12: Residual analysis - II Exercise 13: ARMA get in

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

Exercise 1: ARIMA - integrated ARMA Exercise 2: ARIMA - plug and play Exercise 3: Simulated ARIMA Exercise 4: Global warming Exercise 5: ARIMA diagnostics Exercise 6: Diagnostics - simulated overfitting Exercise 7: Diagnostics - global temperatures Exercise 8: Forecasting ARIMA Exercise 9: Forecasting simulated ARIMA Exercise 10: Forecasting global temperatures

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Exercise 1: Pure seasonal models Exercise 2: P/ACF of pure seasonal models Exercise 3: Fit a pure seasonal model Exercise 4: Mixed seasonal models Exercise 5: Fit a mixed seasonal model Exercise 6: Data analysis - unemployment I Exercise 7: Data analysis - unemployment II Exercise 8: Data analysis - commodity prices Exercise 9: Data analysis - birth rate Exercise 10: Forecasting seasonal ARIMA Exercise 11: Forecasting monthly unemployment Exercise 12: How hard is it to forecast commodity prices?

Ejercicio actual

Exercise 13: Congratulations!