I'm into ML and data science for a while but now I started exploring time-series forecasting, and I have (lets say) a simple question: What are the features/inputs for the time-series forecasting model?
Let's say that I want to predict future sales for a specific store. These are the features that I have:
1.date (feature) 2.number of visitors (feature) 3.sales (result)
I will use this data for training the model, but my question is related to prediction. I can add dates in the future, but I can not add
a number of visitors because I do not know that (I do not know how many people will visit the store in the future). That means that for time-series forecasting, the only input that I can have for future prediction is a date, am I right?
Is there any explanation for this?
For example, If I have a simple classification problem, such as predicting gender based on the weight, height, and shoe number of a person, I will use those features for training, and then I'll add weight, height, and shoe number for 'unknown' person and my model will tell me is it male or female. But for time series forecasting, I can use date and 100 different features for training, but the only feature that I have for future prediction is the date.
Can someone tell me if I'm right or wrong, and please explain to me this kind of 'anomaly'?
FYI I was thinking about starting with Prophet, NeuralProphet, ARIMA models, etc. but eventually ill make my own models with Keras