To a first approximation you can assume the succesive differences are uncorrelated, and then you could try to do a linear fit. Which in this case means an average of the 3rd column. And then you remove the outlier because it has a large Mahalanobis distance.

What you should try to do is filter (e.g., taking successive differences is a filter) the original series untill you get something that presumably is stationary and then fit some sort of ad-hoc model. ARIMA models are ways to reduce the model to some linear regression or other, and you always have the issue of outlier rejection.

A man of words and not of deeds is like a garden full of weeds; a man of deeds and not of words is like a garden full of turds — Anonymous