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.
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Date: June 30, 2022
Software: Time Series Lab - Home Edition
Topics: cyclical patterns in data
Batch program: minks.txt
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
plot Y data ► select the Composite tab and plot Composite signal
select the Individual tab and plot Cycle
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:
- 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 $
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:
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
- [Not statistics] Why would the cycle of Lmuskrat lead the cycle of Lmink by some years?
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.