A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years), but the sampling could be irregular. A time series analysis consists of two steps: (1) building a model that represents a time series (2) validating the model proposed (3) using the model to predict (forecast) future values and/or impute missing values. If a time series has a regular pattern, then a value of the series should be a function of previous values. The goal of building a time series model is the same as the goal for other types of predictive models which is to create a model such that the error between the predicted value of the target variable and the actual value is as small as possible. The primary difference between time series models and other types of models is that lag values of the target variable are used as predictor variables, whereas traditional models use other variables as predictors, and the concept of a lag value doesn’t apply because the observations don’t represent a chronological sequence.