Time series modelling I have daily data for 2.5 years , but with more data points as 0, so when i excluded them in the cases which seems to be invalid. Can i use any other model than models used in time series or should i consider time series with missing values and proceed ?
since they are quite a lot of missing values and am new to time series i dont know how to proceed with this. Any input is highly appreciated.
First idea is to use, instead of an invalid data point, the last valid value. This is done in finance where some stocks are not quoted everyday (and when you want to publish the value of your fund you use the last available value for the stocks).

However, this can be an issue when you have too many missing values (especially for prediction).

You can try Bayesian data augmentation (works somewhat well with time series) but if you say that you have a lot of missing data you have to accept that anything that you do on rather poor quality data will be of limited quality.