How to score the health of a company? i'm currently doing dual apprenticeships. My main mission is to represent the health of a company based on accounting records for multiple companies over multiple years. The part of an accounting record i'm interested in is simply the date and the amount spend or receive. We're in a context of time series.
The objective is to build scores, each one representing a part of the company. I have a few indicators for each one of this company's part.
I didn't find any scientific articles about it, so i proposed an algorithm to do so. Here is it :
For each indicator

Build the mean for this indicator for each company
Use a regressive model to get the trend
Compute the difference to this trend (the objective is to increase the amount of data)
Make a forecast
Build the mean for this forecast
Use a regressive model on this forecast to build the trend
Compute the difference to this trend for each company
Based on this difference, build the score

I know this is not perfect, but after a lot of thinking, that's what i ended up with.
For the forecast part, i'm planning to use an LSTM.
I would like to hear your thought about my algorithm, even if you think it is completely crap. My objective is to improve my skills and to build the best system i can. I'm still a bit lost : should i try to transform the problem and go for more classical, like a simple classifier, or should i do that just for the last part ? Well, i don't know.
Thank you.
EDIT
As Julio made me realize, i forgot to mention one important thing. In France, each one of the record has an identifier, which represent what type of of income/outcome it is. For example : each accounting record with the id 40 are accounting charges. In this example i showed you a simple 2 digits identifier, but it can go up to 8.
To me it looks like your question is more about expert knowledge than ML approach: the main problem is how to build a sensible score based on the available indicators. I have zero knowledge about this domain, but in general this kind of problem requires a lot of back and forth with domain experts in order to correctly specify the task in detail.
However I have one piece of advice which might help (or not, maybe you already know this) for the evaluation of any system you build for this task: assuming that you obtain a predicted score which is meant to represent the future of the company, you could evaluate how well this score matches &quot;the future&quot; as follows:

Split the data at a particular point in time in the past, say 2010
Use only the data before 2010 to train and test a model, i.e. obtain predictions based only on data before 2010
Evaluate how well the predicted scores match the &quot;future&quot; of the companies, i.e. the actual data after 2010. For example a low score should lead to poor indicators, a high score should lead to good indicators (you might need to devise a specific method to correlate the scores with the indicators).