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2. You Should Know about Time Series Forecasting in R

Methods of Time Series Forecasting

  • ARIMA Model

ARIMA stands for Autoregressive Integrated Moving Average. It is a combination of the Autoregressive (AR) and Moving Average (MR) model. The AR model forecast corresponds to a linear combination of past values of the variable. The moving average model forecast corresponds to a linear combination of past forecast errors. The “I” represents the data values that are replaced by the difference between their values and the previous values.

  • SARIMA Model

SARIMA stands for Seasonal Autoregressive Integrated Moving Average. It extends the ARIMA model by adding a linear combination of seasonal past values and forecast errors.

  • VAR

The Vector Autoregression (VAR) method models the next step in each time series using an AR model. The VAR model is useful when you are interested in predicting multiple time series variables using a single model.

  • LSTM

The Long Short Term Memory network or LSTM is a special kind of recurrent neural network that deals with long-term dependencies. It can remember information from past data and is capable of learning order dependence in sequence prediction problems.

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