Sinusoidal model

In statistics, signal processing, and time series analysis, the basic sinusoidal model is:

$Y_{i}=C+\alpha \sin(2\pi \omega T_{i}+\phi )+E_{i}$

where C is constant defining a mean level, α is an amplitude for the sine function, ω is the frequency, T_i is a time variable, and φ is the phase. This sinusoidal model can be fit using non-linear least squares; to obtain a good fit, sinusoidal models require good starting values for C, the amplitude, and the frequency.

Good Starting Value for C

A good starting value for C can be obtained by calculating the mean of the data. If the data show a trend, i.e., the assumption of constant location is violated, one can replace C with a linear or quadratic least squares fit. That is, the model becomes

$Y_{i}=(B_{0}+B_{1}T_{i})+\alpha \sin(2\pi \omega T_{i}+\phi )+E_{i}$

or

$Y_{i}=(B_{0}+B_{1}T_{i}+B_{2}T_{i}^{2})+\alpha \sin(2\pi \omega T_{i}+\phi )+E_{i}$

Good Starting Value for Frequency

The starting value for the frequency can be obtained from the dominant frequency on a spectral plot. A complex demodulation phase plot can be used to refine this initial estimate for the frequency.

Good Starting Values for Amplitude

A complex demodulation amplitude plot can be used to find a good starting value for the amplitude. In addition, this plot can indicate whether or not the amplitude is constant over the entire range of the data or if it varies. If the plot is essentially flat, i.e., zero slope, then it is reasonable to assume a constant amplitude in the non-linear model. However, if the slope varies over the range of the plot, one may need to adjust the model to be:

$Y_{i}=C+(B_{0}+B_{1}T_{1})\alpha \sin(2\pi \omega T_{i}+\phi )+E_{i}$

That is, one may replace α with a function of time. A linear fit is specified in the model above, but this can be replaced with a more elaborate function if needed.

Model validation

As with any statistical model, the fit should be subjected to graphical and quantitative techniques of model validation. For example, a run sequence plot to check for significant shifts in location, scale, start-up effects, and outliers. A lag plot can be used to verify the residuals are independent. The outliers also appear in the lag plot, and a histogram and normal probability plot to check for skewness or other non-normality in the residuals.

External links

• Beam deflection case study

References

 This article incorporates public domain material from the National Institute of Standards and Technology