Analysing *Oil, Gold, and Stocks* between 2012-01-01 and 2012-06-31downloaded from Quandl in *0.724* second with the following original description:

Comparison of oil, gold and stock markets. USD.

This daily dataset contains *483* rows and *4* columns with the overall number of *1932* records, from which *942* records will be analysed for the *Oil* variable.

The descriptive statistics of *Oil* can be used to provide a quick and dirty overview of the data:

min | mean | median | max | sd | IQR |
---|---|---|---|---|---|

77.7 | 96 | 95.5 | 111 | 7.18 | 11 |

The below histogram also shows that the values are somewhere between *77.7* and *111* (range: *32.9*) with the average mean being *96*:

If the data is not normal, it is also worth checking out the median (*95.5*) and the interquartile range (*11*) too instead of the standard deviation (*7.18*).

References:

- Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
*The New S Language*. Wadsworth & Brooks/Cole. - Chambers, J. M. and Hastie, T. J. (1992)
*Statistical Models in S*. Wadsworth & Brooks/Cole. - Venables, W. N. and Ripley. B. D. (2002)
*Modern Applied Statistics with S*. Springer.

The above histogram shows not much about a time-series, right? Let us check out other options.

The daily data between 2012-01-03 and 2013-11-12 on a line-plot:

Which looks much better on a calendar heatmap:

References:

- Paul Bleicher (2009)
*calendarHeat*R function - Jason Bryer (2012) makeR: Package for managing projects with multiple versions derived from a single source repository. R package version 1.0.2. http://CRAN.R-project.org/package=makeR

Computing the cross-correlation of a signal with itself is a mathematical tool for finding repeating patterns in the time-series. Basically we compute the correlation coefficient between the raw data and its lagged version for serveral iterations, where high (>0.5) or low (<-0.5) values show a repeating pattern.

References:

- Venables, W. N. and Ripley, B. D. (2002)
*Modern Applied Statistics with S*. Fourth Edition. Springer-Verlag.

There are not enough (less than 2 periods of) data in the time series, so seasonal decomposition is not attemplted.

And now we build a really simple linear model based on the year, the month, the day of the month and also the day of the week to predict Oil, Gold, and Stocks. The model that can be built automatically is: `value ~ year + month + mday + wday`

.

In order to have reliable results, we have to check if the assumptions of the linear regression met with the data we used:

Â | Value | p-value | Decision |
---|---|---|---|

Global Stat |
180 | 0 | Assumptions NOT satisfied! |

Skewness |
2 | 0.157 | Assumptions acceptable. |

Kurtosis |
16.5 | 4.82e-05 | Assumptions NOT satisfied! |

Link Function |
160 | 0 | Assumptions NOT satisfied! |

Heteroscedasticity |
1.87 | 0.171 | Assumptions acceptable. |

To check these assumptions, the Global Validation of Linear Model Assumptions R-package will help us. The result of that we can see in the table above.

The GVLMA makes a thorough detection on the linear model, including tests generally about the fit, the shape of the distribution of the residuals (skewness and kurtosis), the linearity and the homoskedasticity. On the table we can see if our model met with the assumptions. As a generally accepted thumb-rule we use the critical p-value=0.05.

So let's see the results, which the test gave us:

The general statistic tells us about the linear model, that it does not fit to our data.

According to the GVLMA the residuals of our model's skewness does not differs significantly from the normal distribution's skewness.

The residuals of our model's kurtosis differs significantly from the normal distribution's kurtosis, based on the result of the GVLMA.

In the row of the link function we can read that the linearity assumption of our model was rejected.

At last but not least GVLMA confirms homoscedasticity.

In summary: We can 't be sure that the linear model used here fits to the data.

References:

- Pena, EA and Slate, EH (2006): Global validation of linear model assumptions.
*J. Amer. Statist. Assoc.***101**(473):341-354.

As we want to fit a linear regression model, it is advisable to see if the relationship between the used variables are linear indeed. Next to the test statistic of the GVLMA it is advisable to use a graphical device as well to check that linearity. Here we will use the so-called crPlots funtion to do that, which is an abbreviation of the Component and Residual Plot.

First, we can see two lines and several circles. The red interrupted line is the best fitted linear line, which means that te square of the residuals are the least while fitting that line in the model. The green curved line is the best fitted line, which does not have to be straight, of all. The observations we investigate are the circles. We can talk about linearity if the green line did not lie too far from the red.

References:

- Cook, R. D. and Weisberg, S. (1999)
*Applied Regression, Including Computing and Graphics.*Wiley. - Fox, J. (2008)
*Applied Regression Analysis and Generalized Linear Models*, Second Edition. Sage. - Fox, J. and Weisberg, S. (2011)
*An R Companion to Applied Regression*, Second Edition, Sage.

Â | Estimate | Std. Error | t value | Pr(>|t|) |
---|---|---|---|---|

d$year |
3.86 | 0.627 | 6.15 | 1.63e-09 |

d$month |
-0.449 | 0.0962 | -4.66 | 4.06e-06 |

d$wday |
-0.0127 | 0.222 | -0.0573 | 0.954 |

d$mday |
-0.0128 | 0.0355 | -0.36 | 0.719 |

(Intercept) |
-7672 | 1263 | -6.08 | 2.56e-09 |

Most model parameters can be read from the above table, but nothing about the goodness of fit. Well, the R-squared turned out to be *0.126* while the adjusted version is *0.119*.

References:

Chambers, J. M. (1992)

*Linear models.*Chapter 4 of*Statistical Models in S*eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.Wilkinson, G. N. and Rogers, C. E. (1973) Symbolic descriptions of factorial models for analysis of variance.

*Applied Statistics*,**22**, 392-9.

Let us also check out the residuals of the above linear model:

References:

- Belsley, D. A., Kuh, E. and Welsch, R. E. (1980)
*Regression Diagnostics.*New York: Wiley. - Cook, R. D. and Weisberg, S. (1982)
*Residuals and Influence in Regression.*London: Chapman and Hall. - Firth, D. (1991) Generalized Linear Models. In Hinkley, D. V. and Reid, N. and Snell, E. J., eds: Pp. 55-82 in Statistical Theory and Modelling. In Honour of Sir David Cox, FRS. London: Chapman and Hall.
- Hinkley, D. V. (1975) On power transformations to symmetry.
*Biometrika**62*, 101-111. - McCullagh, P. and Nelder, J. A. (1989)
*Generalized Linear Models.*London: Chapman and Hall.

At last, let us compare the original data with the predicted values:

References:

- Chambers, J. M. and Hastie, T. J. (1992)
*Statistical Models in S*. Wadsworth & Brooks/Cole.

Here we try to identify the best ARIMA model to better understand the data or to predict future points in the series. The model is chosen according to either AIC, AICc or BIC value is:

Damn, we could not fit a model:

```
We are terribly sorry, but this computational intensive process
is not allowed to be run on a time-series with more then 365 values.
Please sign up for an account at rapporter.net for extra resources
or filter your data by date.
```

References:

- Hyndman, R.J. and Khandakar, Y. (2008) "Automatic time series forecasting: The forecast package for R",
*Journal of Statistical Software*,*26*(3).