What are the types of time series?

Two of the most common models in time series are the Autoregressive (AR) models and the Moving Average (MA) models . The autoregressive model uses observations from preivous time steps as input to a regression equations to predict the value at the next step.

Examples of time series forecasting Forecasting the closing price of a stock each day . Forecasting product sales in units sold each day for a store. Forecasting unemployment for a state each quarter. Forecasting the average price of gasoline each day.

Time series analysis is used to identify the fluctuation in economics and business . It helps in the evaluation of current achievements. Time series is used in pattern recognition, signal processing, weather forecasting and earthquake prediction.

Time series data can be classified into two types:

These four components are:

Types of time series forecasting methods include:

Identifying Patterns in Time Series Data. Interrupted Time Series . Exponential Smoothing . Seasonal Decomposition (Census I)