I am working on a time series, regression problem, where I have 10 features and 180 observations. I would like to understand what the minimum number of features should be in a dataset to use feature selection method?
For starters you can find the correlation of each column with the output column and select the features which are highly correlated .This will also help you to remove features which will not contribute towards learning weights and biases . For example
fixed acidity 0.119024 volatile acidity -0.395214 citric acid 0.228057 residual sugar 0.013640 chlorides -0.130988 free sulfur dioxide -0.050463 total sulfur dioxide -0.177855 density -0.184252 pH -0.055245 sulphates 0.248835 alcohol 0.480343 quality 1.000000 Name: quality, dtype: float64
Above code will give the correlation of the output label with each column.Removing columns with negative correlation with increase you accuracy .By doing this you can also choose you top 5 or 10 feature which are highly correlated with the output label and include them in your model.