I am new to this Regression world and I have a nerd question, you may say.
Actually I was trying to solve a problem to predict future sales in my organization. I have collected all the data for last year. My data includes (for each day):
- Total Sales(count)
- Temperature
- Wind Direction
- Precipitation
- Day of week (i.e 1 or 2 or 3.. or 7)
- Whether a working day or not. etc.
My goal : 1. I will train a model so that if I give the input of all the values of 2 to 7 (i.e of data, of the day that I want to predict, which is neither in test nor test data) and it will give me the predicted value of 1 (i.e Total Sales).
I Tried : 1. 1st I tried with a Univariate LSTM model(i.e with total sales from past one year data, predict the next data). But, I couldn't feed the other data as input.
Then I tried a Multivariate LSTM model, but this would predict all of the data for the next series.
Then I searched for many tutorials to solve the problem. Such as : This video tutorial which uses LSTM for electricity bill consumption, but it only shows the model building and not how to implement it.
I came with another question : from stack overflow. But here, the user seems to be moving to reinforcement learning.
Conclusion : What should i do to solve such problems? How to predict future sales count by feeding the data for that day?
