# Predicting time series value given a threshold weight

I have 2 datasets. One is time series data of sale of homes by region by type:

Region    Type    SaleMonth   Sales
NE        SF      201601      100
NE        SF      201602      100
NE        SF      201603      150
NE        SF      201604      100
NE        SF      201605      150


These data were manually entered by realtors and given to us by a data feed.

We also get another data set of registered homes from the county. However there is a 2-month lag for this data set. It is structured as follows:

Region    Type    SaleMonth   Sales
NE        SF      201601      130
NE        SF      201602      130
NE        SF      201603      160
NE        SF      201604
NE        SF      201605


The weight is calculated as: Retail Transactions/Registered values.

So, assuming the weight is correct, I have to predict the Registered values for 201604 and 201605. I have tried regression, time series analysis, etc., but I wasn't able to get to the threshold weight.

Is this a candidate for neural net time series? Is there anything else I can do assuming the weight is correct.

Assuming it is incorrect, what is the best approach to determine what sliding window of values to use from the transactions to accurately predict the registered values?

• Can you clarify the "weight"? I don't see "Retail Transactions" or "Registered values" in either dataset. What do you mean that you weren't "able to get to the threshold weight"? May 31 '16 at 16:24
• Thank you gung for your response. The first table is Retail Transactions and the second table is Registered Values. Weight is defined as: Retail sales/registered sales.
– Bee
May 31 '16 at 16:50
• So you want to determine if the ratio of the sales variables in the two tables is roughly constant? What do you mean that you weren't "able to get to the threshold weight"? May 31 '16 at 16:55