Monthly Times Series Modeling Approach I have a machine learning problem and have been working in Sklearn/Pandas with Python to come up with an accurate model. I find myself deep in a rabbit hole trying to learn the best approach and how many variables are too many variables while trying to avoid overfitting.  
Each model is for a different area with the variables indicated below: 
x = monthly precipitation departure (this can be used as overall monthly averages over an area, or can be broken down into sub-areas from the overall area of interest to add additional variables) For example Kansas Group 1 can be treated as a whole or could be separated into sub-areas with monthly averages for each area.
y = monthly availability of a resource (eg. Jan = 0.003827)
n = 16 years or 192 months of data
I have tried many different approaches. 
The first approach was modeling each month separately so a model for January (n=16) and a model for February (n=16) etc. using the following modeling techniques:


*

*LinearRegression using my own assigned weighting variables as a
weighted running mean analysis


*

*RandomTrees with tuning variables

*RandomForest with tuning variables

*ExtraTrees with tuning variables



Then in order to try and improve the model, I most recently employed a time series model (n=192)
 - SARIMAX with tuning variables p,d,q,P,D,Q,12
Any advice or resources are greatly appreciated.
 A: Time series analysis (ARIMA) is often flawed when the original series contains deterministic structure see @AdamO's wise reflections Interrupted Time Series Analysis - ARIMAX for High Frequency Biological Data? . The arima model building/identification process as part of a Transfer Function analysis is not a one and done but rather a sequence of steps to form a useful model following the paradigm presented here Is it possible to automate time series forecasting? . This is easily extended to include user-specified X series leading to a Transfer Function.
In general your mission is to form a model following the thread ( particularly the Tsay article ) as discussed here Why to use ARMA model as a time series is either over-differenced or under-differenced? 
and broadly here https://stats.stackexchange.com/search?q=user%3A3382+intervention+detection
Finally https://autobox.com/pdfs/vegas_ibf_09a.pdf slide 16- illustrates the flaw of not dealing with pulses as one can be lead down the path of unwarranted power transformations for the now famous AIRLINE SERIES ( 144 monthly values) .
Using lagged explanatory variables to forecast future value of depended speaks to your issue with respect to how to use helping X's that are pre-specified and to identify latent structure reflecting/proxying OMITTED series.
EDITED AFTER RECEIPT OF DATA:
I took your 192 values and partitioned then to 156 and 36 and obtained a Weighted Mape of 21.2% .  and 
The model included a log xform and an arima of (1,0,0) along with 7 seasonal pulses refelecting a monthly determiistic structure while incorporating your predictor variable contemporaneously and adjusting for 7 anomalies (pulses).
The residuals from this model suggested randomness .
The Actual/Fit and Forecast graph is here


Finally a derailed loo at the 36 period forecasts vis-s-vis the actual is here 

