# Case Studies

If you're interested in time series analysis and forecasting, this is the right place to be. The Time Series Lab (TSL) software platform makes time series analysis available to anyone with a basic knowledge of statistics. Future versions will remove the need for a basic knowledge altogether by providing fully automated forecasting systems. The platform is designed and developed in a way such that results can be obtained quickly and verified easily. At the same time, many advanced time series and forecasting operations are available for the experts. In our case studies, we often present screenshots of the program so that you can easily replicate results.

Did you know you can make a screenshot of a TSL program window? Press Ctrl + p to open a window which allows you to save a screenshot of the program. The TSL window should be located on your main monitor.

Click on the buttons below to go to our case studies. At the beginning of each case study, the required TSL package is mentioned. Our first case study, about the Nile data, is meant to illustrate the basic workings of the program and we advise you to start with that one.

# Minks

Date: June 30, 2022

Software: Time Series Lab - Home Edition

Topics: cyclical patterns in data

#### Minks and muskrats

In this case study we illustrate how to model the presence of a cycle using the numbers of furs of minks and muskrats traded annually by the Hudson Bay Company in Canada from 1848 to 1909. The main issue concerns the existence of a cycle in the pattern of traded furs of both minks and muskrats. Parts of this case study are from the STAMP manual.

The MINKMUSK dataset is part of the TSL installer and can be found in the data folder located in the install folder of TSL.
It is instructive to carry out a preliminary analysis on the data.
Go to the **Database** section and load the data in TSL and plot it by clicking the **Lmink** (L for logarithm) header in the database section. Click the **autocorrelation function** button in the bottom right of the screen. The autocorrelation function shows evidence of a cycle buried within the noise. The **spectral density** shows the same message. On this graph, the period is given by dividing 2 by the scaled frequency, on the horizontal axis. Thus there appears to be a cycle with a period of around 10 years.

Proceed to **Build your own model** and select a **Fixed Level**, a **Stochastic Slope** and a **Cycle** component.
Before you estimate the model, go to the Graphics and Diagnostics page and switch **Add lines to database** on.

**Estimation page** ► Estimation ends at t = 1909 ► Estimate

Once the estimation has been finished we are automatically brought to the **Graphics and diagnostics** section.

**Graphics and diagnostics** ► **Clear all** ► select the **Individual** tab ► plot **Y data** and **Level**

Add subplot

plot **Y data** ► select the **Composite** tab and plot **Composite signal**

Add subplot

select the **Individual** tab and plot **Cycle**

Add subplot

select the **Residuals** tab, select Smoothing, and plot **Residuals**

#### Extracted components from Mink data

It can be seen in the Figure above that the estimated trends are relatively smooth. The smoothness arises because the level variances were constrained to be zero and the q-ratio (signal-to-noise ratio) for the slope are small.
More precise information about the fitted cycle can be found in the written output on the **Text output** page.

The written output shows us (among other things) the **Cycle properties**:

Parameter type | Cycle 1 |
---|---|

Variance | 0.0642 |

Period | 9.6477 |

Frequency | 0.6513 |

Damping factor | 1.0000 |

Amplitude | 0.3550 |

- A variance parameter which is responsible for making the cycle stochastic
- A period (in years), $\lambda $
- A frequency (in radians) $2 \pi / \lambda$
- A damping factor $\rho $

**Amplitude**. For data in logarithms, the amplitude of the cycle is a percentage of the trend. This is for Lmink 35.5%.

Now let's turn our attention to the **Lmuskrat** series. Repeat all steps as described above but now for the Lmuskrat series. We obtain the following cycle properties:

Parameter type | Cycle 1 |
---|---|

Variance | 0.2110 |

Period | 10.6821 |

Frequency | 0.5882 |

Damping factor | 0.8309 |

Amplitude | 1.0565 |

Let's investigate the cycles of **Lmink** and **Lmuskrat** by plotting the cycles in one graph.
For this we explore the **Add plots to database** functionality on the **Graphics and diagnostics** page. This functionality can be switched on and off with the switch in the top middle of the **Graphics and diagnostics** page. When switched on, every component that is plotted is stored in the **Database** on the **Database** page.

Go to the **Database** page and click on *Cycle: Lmink* followed by Ctrl-click *Cycle: Lmuskrat*.
Both cycles are now plotted in one graph and from this you can see that the cycle extracted from the **Lmuskrat** series leads the cycle of **Lmink** by a couple of years.

#### Extracted cycles of Lmink and Lmuskrat

#### Further exploration

- [Not statistics] Why would the cycle of
**Lmuskrat**lead the cycle of**Lmink**by some years?

# Bibliography

### References

Koopman, S.J., A.C. Harvey, J.A. Doornik, and N. Shephard (2012). STAMP: Structural time series analyser, modeller and predictor. *London, Timberlake Consultants Ltd.*