[Project 2] Day3: Time Series Forecasting contd…

I continued my study for timeseries forecasting. Bellow is what I learnt:

• There are 11 different classical time series forecasting methods which are:
  1. Autoregression (AR)
  2. Moving Average (MA)
  3. Autoregressive Moving Average (ARMA)
  4. Autoregressive Integrated Moving Average (ARIMA)
  5. Seasonal Autoregressive Integrated Moving-Average (SARIMA)
  6. Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX)
  7. Vector Autoregression (VAR)
  8. Vector Autoregression Moving-Average (VARMA)
  9. Vector Autoregression Moving-Average with Exogenous Regressors (VARMAX)
  10. Simple Exponential Smoothing (SES)
  11. Holt Winter’s Exponential Smoothing (HWES)
• Out of these, the three below are the ones on which i did an in-depth reading.
• ARIMA stands for Autoregressive Integrated Moving Average.
  • The following step in the sequence is predicted by the Autoregressive Integrated Moving Average (ARIMA) technique model as a linear function of the differenced observations and residual errors at earlier time steps.
  • In order to make the sequence stable, the method combines the concepts of Moving Average (MA) and Autoregression (AR) models with a differencing pre-processing phase known as integration (I).
  • For single-variable time series with a trend but no seasonal changes, the ARIMA method works well.
• The full-form of VAR is Vector Auto Regression
  • Using an AR model approach, the Vector Autoregression (VAR) method models each time series’ subsequent step. In essence, it expands the AR paradigm to accommodate several time series that are parallel, such as multivariate time series.
  • The model’s nomenclature entails passing a VAR function’s parameters, such as VAR(p), as the order for the AR(p) model.
  • Multivariate time series devoid of trend and seasonal components can benefit from this strategy.
• Holt Winter’s Exponential Smoothing (HWES)is also called the Triple Exponential Smoothing method.
  • It models the next time step as an exponentially weighted linear function of observations at prior time steps, taking trends and seasonality into account.
  • The method is suitable for univariate time series with trend and/or seasonal components.