# What are some appropriate models to use for inventory forecast based on consumption history or trends?

I am working on an inventory management system where I have daily/monthly/yearly consumption history for a particular item, which may or may not follow a repeating trend. In order to forecast demands within an upcoming time-frame, which model / distributions are recommended?

Data Format: Data is a series of (x, y) pairs where x is a timestamp and y is count of consumption.

As far as I studied, random forest or tree based models do not handle trend patterns effectively, while linear models do not seem to be appropriate either given the fact that data do not seem to be following a linear pattern.

Is this problem solvable using a single model? Should Linear Regression models be used for this problem?

• It depends on your data, could you plot of your data, and post the graph? but you can use Autoregressive models, such as arima or (since we are talking about stock which is changes with sales) VAR models could also be an appropriate type of model. Depends on your data but you can do better than linear regression. Nov 13 '18 at 15:20