Cross-validation for time series regression model I have the following datasets of several variables:


*

*hourly data of the year 1990

*hourly data of the year 2000

*hourly data of the year 2010


Now, I was planning to select and estimate a linear regression model based on the dataset of the year 1990 and then validate/check its accuracy by applying  the model to the other two datasets and compare the dependent variable based on the regression model with the actual dependent variable of the dataset.
Is this approach correct? If so - am I supposed to completely ignore the two validation datasets? By that I mean not even looking at it and doing descriptive analysis?
Note: All the datasets contain the same variables. 
 A: 
Is this approach correct?

It depends on the data, but most likely the answer is "no this is not correct". There are very likely trend effects which would 2010 and 2000 different from 1990. 
You might want to identify a trend between the 3 years, and then use that to de-trend the data, but that would only work if the trend is linear and even then you are making some strong assumptions, because of the large gap between the 3 samples. 
Since you are dealing with a year's worth of hourly data, there are likely multiple seasonalities within your data (daily patterns, weekly patterns and monthly patterns). It depends on your data, but it might possible to assume that these patterns haven't change over the years. You might be able to normalize your data relative to yearly averages. Then you would be able to use 2000 and 2010 for validation. 
Consider the following time series of international airlines passengers shown below: 
The seasonality for 1954 and 1959 are very similar, but the level is much higher for 1959 because of the visible trend in the data. So if you trained a model on 1954 data and then tried to validate it using 1959 data, it would be completely off. 
But if you were to de-trend the data or normalize it to the yearly average, then a model trained on 1954 data might do a better job at forecasting 1959 data. 

