* Time series data can be thought of as an extension to linear regression which uses terms like autocorrelation, movement of averages for summarizing historical data of y-axis variables for predicting a better future.
* Forecasting and prediction is the main goal of time series problems where accurate predictions can be made but sometimes the underlying reasons might not be known.
* Having Time in the problem does not necessarily mean it becomes a time series problem. There should be a relationship between target and time for a problem to become a time series problem.
* The observations close to one another in time are expected to be similar to the ones far away which provide accountability for seasonality.
* For instance, today’s weather would be similar to tomorrow’s weather but not similar to weather from 4 months from today. Hence, weather prediction based on past data becomes a time series problem.
