# Choosing the right model for predicting demand

We have a data set of 300,000+ records that looks something like the following:

Item ID, Quantity 2017, Quantity 2016, Quantity 2015, Quantity 2014, Quantity 2013
1111, 100, 50, 25, 10, 0
2222, 0, 10, 100, 500, 1000
3333, 10, 0, 5, 2, 4


We are currently attempting to find the best model to predict Quantity 2017 based on the previous quantities for each record. We have tried Decision Tree Regression, Multiple Linear Regression, and Random Forest Regression (10, 100, 1000 trees) but our results were a bit too far off for approval. We are using 80% of our data for training and 20% for testing.

Are there any models that are better suited for this type of calculation?

We are also concerned that perhaps our data structure is part of the problem and we should re-evaluate. Is there another structure that might be better?