Have a look at the Python code below. It calculates the auto-correlation of the data, after the DC part of the signal was removed. The autocorrelation shows nice peaks with much less noise than the original signal. Then, we detect the local maxima and measure the distance between them. This distance is then the estimated period of your signal:
import csv
import requests
import numpy as np
from io import BytesIO
from scipy import signal
url = 'http://pastebin.com/raw/5ghKbBRm'
response = requests.get(url)
A = np.genfromtxt(BytesIO(response.content)) # read the CSV file
# begin signal processing
A = A - np.mean(A) # remove the DC part of the signal
plt.subplot(121)
plt.plot(A)
plt.subplot(122)
C = np.correlate(A, A, 'same') # calculate autocorrelation to smooth out the noise
plt.plot(C)
plt.subplot(122)
P = signal.argrelextrema(C, np.greater)[0] # caluclate local maxima (this step can be improved)
P = P[C[P]>15000] # filter maxima, where the autocorrelation values is too low
plt.stem(P, 30000*np.ones_like(P)) # plot where the detected peaks are
print ("Peak distances: ", np.diff(P)) # calculate distance between peaks and the mean of the distances
print ("Estimated period: ", np.mean(np.diff(P)))
Program output:
Peak distances: [117 118 117 117 117 117 118 117]
Estimated period: 117.25
There is room for improvement. For example, the detection of local maxima is not very robust and the threshold was set manually, but this should enable you to find out the rest on your own.
