ARIMA

ARIMA

ARIMA

Description

ARIMA stands for Auto-Regressive Integrated Moving Average. It is used for forecasting on stationary data.

Why to use

ARIMA captures both autoregressive and moving average components, which provides a systematic way to deal with non-stationary data by differencing. Also it is effective for predicting future values based on past trends and patterns in the data.

When to use

  • When you have a stationary or near stationary series.
  • when there is significant autocorrelation in the data

When not to use

  • On seasonal data
  • Non-linear relationship in data
  • High frequency noise in data

Prerequisites

  • The data should be stationary.

Input

  • Any time series data.

Output

  • Forecasting Chart
  • Trained model parameters
  • Accuracy

Statistical Methods Used

  • Moving averages

Limitations

  • Non-linear data
  • Sensitivity to outliers
  • Non-stationarity of data

ARIMA or Autoregressive Integrated Moving Average is an extension of ARMA model. The ARIMA model can handle non-stationary data, that is time dependent data more effectively. ARIMA converts the non-stationary data into stationary data by performing differencing.

Differencing refers to subtracting the previous observation from the current observations. The differencing is performed until the data becomes stationary.

    • Related Articles

    • Auto ARIMA

      Auto ARIMA Description Auto ARIMA (Auto-Regressive Integrated Moving Average) is a statistical algorithm that uses time series data to forecast future values. It automatically determines the best parameters for an ARIMA model. Why to use Auto ARIMA ...

    • Modeling

      Models for time series data can have many forms and represent different stochastic. When modeling variations in the level of a process, three broad classes of practical importance are the AutoRegressive (AR) models, the Integrated (I) models, and the ...

    • SARIMA

      SARIMA Description SARIMA is an abbreviation for Seasonal Autoregressive Integrated Moving Average. It is an extension of ARIMA used to model seasonal time series. SARIMA considers the events occurring at regular intervals and impacting the target ...

    • Rubiscape Winter '19

      New Features Platform & Studio New dataset creation feature for Twitter, PostgresSQL, SQL, MySQL, Oracle, Excel, CSV, Google News. Create dataset from a local TXT file using delimiter option. Supported delimiters are Semicolon, Pipe, Comma, Tab, ...

    • rubiscape Concepts

      Code Fusion Code fusion in rubiscape gives an ingenious option to the users to build their models in programming languages such as JAVA, R, or Python and integrate them into rubiscape. Code fusion makes the rubiscape platform more customizable and ...