Artificially Increasing Training data I am trying to predict sales quantity based on attributes of the item.Sales are aggregated by week wise and prediction is also done by week wise.I am having large number records with zero sales quantity than compared to positive sales(for 20 positive sales 250 zero sales records are there).I want to increase my training data by adding small values to sales quantity and combining with total records. At present I am using RANDOM FOREST and NEURAL NETWORK .I am not getting any good results Please correct me if anything is not considerable.
Can anybody suggest way to increase my training data
 A: If your sales are aggregated by week then you data is most likely highly autocorrelated. You can test whether autocorrelation in your data is significant that with a simple autocorrelogram or a Ljung-Box test.
Cross-sectional methods such as standard neural networks and random forest cannot handle a high degree of autocorrelation. You will need to apply a time series approach such as ARMA models, exponential smoothing, autoregressive neural networks, or something equivalent.  
A: at first I don't quite understand the meaning of "increase my training data by adding small values to sales quantity", I guess you mean  adding items that are barely sold to the positive sales category. I think that if your goal is to find a robust model to distinguish items that can sell well, you should not mix in not well sold items to the postive category. 
A solution to get better accuracy is to resolve the issue of unbalanced data and probably try some other models/ with more aggressive parameter tuning(searching). I would start with resampling(repeating) the positive sales samples so that its case number is roughly the same as the negative sales sample number. This strategy works surprisingly well esp if you are using boosting in building the decision tree.  
