model to predict annual outcome based on previous years data I have below datasets for two years each holding about 10.000 records. Every week a new report is generated that shows the performance for the current or any previous month. Therefore a more recent weekly report would overwrite the monthly value of a previous report (for that specific month). I need to predict this year's performance based on the weekly data coming in.



I have two questions:


How would I need to structure my dataset for machine learning? If I
keep the above I would get about 150 features (3 per week) and I do not
know whether there is even a ML algorithm that can handle the many n/a (all
forthcoming weeks) for the year for which I need to run the
prediction. I can also transform this into a new dataset by combining
the three columns by week into three features (would result in long 
narrow table). But that would replicate each entity and output value about 50 times (once per
week).
Based on the answer to 1. what would be suitable training
algorithms?
I guess you need to predict following week's performance based on previous month's performance. Well, I think its more or less like predicting tomorrow's weather based on previous data.

I guess you need to use Time series forecasting model (ARIMA or LSTM ) for the same by setting the lag for one month. You can build a simple Deep Learning model for the same by adding layers with lag as a parameter