Python developer specialising in multivariate time series forecasting

I have been tasked to build a multivariate time series forecast using Python. I am still flexible as to the best algorithm to use, however, have identified VAR, LSTM and Facebook Prophet. I require the following help: - identify and confirm my target variable - identify the best algorithms to use based on my data (+/- 60 features) - write the python code to build out the multivariate time series forecast in python - need to complete this by 29 Nov Additional info The fields and explanation below describe my dilemma in identifying my target variable. Attorney_Code - Attorney nominated by the bank to get the home loan registered Customer_Code - Each customer gets a unique code based on the loan applied for Application_Code - customer can make several loan applications Attorney_Date - Date when the loan was received by the attorney Attorney_Amount - Value of the loan as at the attorney date Registered_Date - Date when the loan finally gets registered by the attorney Registered_Amt - final value of the loan paid to the customer Loan_Type - specifies the purpose of the loan Region - specifies the geographic location of the customer applying for the loan Based on the Attorney_Date (known value), I need to be able to predict the Registered_Date (unknown value). However, many of the multivariate time series problems that I have come across use the target variable (i.e. Registered Date) to make further predictions. However, here I need to use the Attorney_date and forecast the Registration_date and thus my confusion of my target variable. In the main dataset there are +/- 60 fields and I also need to understand the best algorithm to use in doing the multivariate time series forecast in Python.