Discrete time series forecasting

1. What is a discrete time series?
2. What are the types of time series forecasting?
3. What is the difference between discrete and continuous data?
4. What are the four types of time series?

What is a discrete time series?

A discrete time series consists of data points separated by time intervals that are greater than one second. A discrete time series might have: A data-reporting interval that is infrequent (e.g., 1 point per minute) or irregular (e.g., whenever a user logs in)

What are the types of time series forecasting?

Types of time series methods used for forecasting

Common types include: Autoregression (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving-Average (SARIMA).

What is the difference between discrete and continuous data?

The primary difference, though, between discrete and continuous data is that discrete data is a finite value that can be counted whereas continuous data has an infinite number of possible values that can be measured.

What are the four types of time series?

Time series are monthly, trimestrial, or annual, sometimes weekly, daily, or hourly (study of road traffic, telephone traffic), or biennial or decennial.