Periodic Pattern recognition In a Sales Log
These are the steps you need to follow.


Model

You need to pick a model that you think explains your data. It can be very simple (e.g. linear regression) or very complex (e.g. a four-layer neural network). You want to pick as simple a model as possible!

Example: Assume sales follow $s(x) = a + b\sin(cx)$.
Train

Using one part of the data, you determine which parameters of the model fit your data best. The method of training depends on your model of choice.

Example: You fit the parameterized sine against the first half of your data with least-squares and find $(a,b,c) = (3,77, 13)$ is "best".
Validate

Inspect how well the model explains the rest of the data. You will have to decide in step 1 how you quantify quality here, and what your acceptance criteria are.

Separately, if that makes sense in your domain, apply the model entirely different data sets that should follow the same model if your hypotheses are correct. Does it predict anything there? What does the answer say about your model and/or your assumptions?

Example: Inspecting the squared error between $3 + 77\sin(13x)$ and the second half of the timeline data, you notice it is far too large.
Iterate

Unless the predictive quality of your model is good, go back to step 1. Sometimes, more training data can be the answer, too; note how that weakens your result, though.