time series
So I plotted the ACF/PACF of oil returns and was expecting to see some positive autocorrelation but to my surprise I only get negative significant autocorrelation. How should I interpret the above graph? They seem to indicate that there is a tendency for oil returns to increase when it decreased previously and vice-versa, thus the oscillating behaviour. Please correct me if I'm wrong.
Those plots are showing you the $\textit{correlation of the series with itself, lagged by x time units}$. So imagine taking your time series of length $T$, copying it, and deleting the first observation of copy#1 and the last observation of copy#2. Now you have two series of length $T-1$ for which you calculate a correlation coefficient. This is the value of of the vertical axis at $x=1$ in your plots. It represents the correlation of the series lagged by one time unit. You go on and do this for all possible time lags $x$ and this defines the plot.
The answer to your question of what is needed to report a pattern is dependent on what pattern you would like to report. But quantitatively speaking, you have exactly what I just described: the correlation coefficient at different lags of the series. You can extract these numerical values by issuing the command
acf(x.ts,100)$acf.
In terms of what lag to use, this is again a matter of context. It is often the case that there will be specific lags of interest. Say, for example, you may believe the fish species migrates to and from an area every ~30 days. This may lead you to hypothesize a correlation in the time series at lags of 30. In this case, you would have support for your hypothesis.
looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses in the data AND 2) when the arima model has constant parameters over time AND 3) when the error variance from the arima model has constant variance over time. When do these three things hold .... in most textbook data sets presenting the ease of arima modelling. When do 1 or more of the 3 not hold .... in every real world data set that I have ever seen . The simple answer to your question requires access to the original facts ( the historical data ) not the secondary descriptive information in your plots. But this is just my opinion!
EDITED AFTER RECEIPT OF DATA:
I was on a Greek vacation (actually doing something other than time series analysis) and was unable to analyse the SUICIDE DATA but in conjunction with this post. It is now fitting and right that I submit an analysis to follow up/prove by example my comments about multi-stage model identification strategies and the failings of simple visual analysis of simple correlation plots as "the proof is in the pudding".
Here is the ACF of the original data 
The PACF of the original series 
. AUTOBOX http://www.autobox.com/cms/ a piece of software that I have helped developed uses heuristics to identify a starting model In this case the initially identified model was found to be 
. Diagnostic checking of the residuals from this model suggested some model augmentation using a level shift, pulses and a seasonal pulse Note that the Level Shift is detected at or about period 164 which is nearly identical to an earlier conclusion about period 176 from @forecaster. All roads do not lead to Rome but some can get you close ! 
. Testing for parameter constancy rejected parameter changes over time . Checking for deterministic changes in the error variance concluded that no deterministic changes were detected in the error variance.
. The Box-Cox test for the need for a power transform was positive with the conclusion that a logarithmic transform was necessary.
. The final model is here 
. The residuals from the final model appear to be free of any autocorrelation 
. The plot of the final models residuals appears to be free of any Gaussian Violations 
. The plot of Actual/Fit/Forecasts is here 
with forecasts here 
Best Answer
Negative ACF means that a positive oil return for one observation increases the probability of having a negative oil return for another observation (depending on the lag) and vice-versa. Or you can say (for a stationary time series) if one observation is above the average the other one (depending on the lag) is below average and vice-versa. Have a look at "Interpreting a negative autocorrelation".