There's not nearly enough information to suggest a model, or to judge if a model might be too simple. With so little data, subject knowledge (how that particular kind of data tends to behave) becomes critical.
In an interview, you might take the strategy of suggesting several potential models - "If there's expected to be strong seasonality, and not a strong trend, maybe you could do this; if strong seasonality and strong trend seem likely, maybe do that; if seasonality and trend would be expected to be weak and noise high, perhaps do this ..." and so on.
(Though if it were me, I'd narrow it down finer than that.)
One might then say something like "if we really don't know what it is we're dealing with, and with little data, very simple models tend to forecast better than complex ones; perhaps exponential smoothing or double exponential smoothing might be one choice if we don't have more indication of what kind of model might be suitable."
(Added later in response to the request in comments)
As support for the claim that "very simple models tend to forecast better than complex ones" (particularly with little data), see for example, Makridakis and Hibon (2000) [1], discussing Makridakis and Hibon (1979) [2]:
The major conclusion of the Makridakis and Hibon study was that simple methods, such as exponential smoothing, outperformed sophisticated ones.
The statement was controversial in 1979, but results from the subsequent M-competitions broadly supported that conclusion (though the statements became somewhat more nuanced); similar sentiments can be found (for example) in the forecasting book by Makridakis, Wheelwright and Hyndman.
More broadly, see (for example) Green and Armstrong (2016) [3]:
Our review of studies comparing simple and complex methods — including those in this special issue — found 97 comparisons in 32 papers. None of the papers provide a balance of evidence that complexity improves forecast accuracy. Complexity increases forecast error by 27 percent on average in the 25 papers with quantitative comparisons. The finding is consistent with prior research to identify valid forecasting methods: all 22 previously identified evidence-based forecasting procedures are simple
[More recently, averages of forecasts have in many cases been found to perform quite well, but again those model-average forecasts have often tended to average over fairly simple models]
[1] Makridakis, S and Hibon, M (2000).
"The M-3 Competition: results, conclusions, and implications".
International Journal of Forecasting, 16 (October–December), 451-476
[2] Makridakis, S., & Hibon, M. (1979).
Accuracy of forecasting: an empirical investigation (with discussion).
Journal of the Royal Statistical Society A, 142, 97–145
[3] Kesten C. Green, K.C., and Armstrong, J. S. (2015),
Simple versus complex forecasting: The evidence
March 1, 2015
(forthcoming in Journal of Business Research)
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2643534
Best Answer
Time series expect you to provide sufficient observations to detect seasonality or trend phenomenons. If you lack data, or if there are simply no existing trend or seasonality, you cannot expect the model to find one. Let $X_{t+1}$ be the value you want to predict and $\hat{X}_{t+1}$ your prediction. For such cases, time-series model will return most of the time either :
You may accept the model returned by an algorithm, or test one that appears more pertinent to you. While exploring your data, you can make your own assumptions, and test them using your judgement. For instance.
Also, do you have any additional data (features) associated to the time series ? Machine learning is useful when you have a lot of observations, and a significant number of features from which you are unable to distinguish a simple pattern. With 24 points, I highly doubt that you will find anything complex, and may as well go through a manual analysis.
Keep in mind that these assumptions can be hardly tested. You do not have a sufficient range (e.g. 3 years) to build a "model" on the two first years, and test it on the last one.
Bottom line : with limited data, you can perform only limited analysis.