# Importance of Time Features

if you have a time series and you want to do some predictions, what time feature should you use ?

lets say we are trying to predict how many people visit a certain website, we have data for the visits for the last 2 years, what should we include as a time feature ? and if we use more features might it make our model less accurate ?

the features i can think of are the following:

Date index(1-700)
Week number (1-53)
Day of the week (1-7)
Month(1-12)
Day of the month(1-31)
Day of the year(1-365)
Year


If you use Week number (1-53) and Day of the year(1-365) in the same model, you will have collinearity. You can derive the week number from the day of the year, so the former doesn't add any additional information.
• Are you trying to predict the number of visits in a given day? Which algorithm are you using for prediction? Date Index (t) relates an observation taken at t to t-1 or t+1, etc. It is your temporal reference point. I guess its importance would rely on the model you wish to use. – Zhubarb Jun 12 '14 at 9:51