Forecasting is used to predict time series data using trends and seasonality factors. Examples include sales data, web traffic data, or social media activity data. The target variable is numeric and stored in time sequence. More information can be found at http://en.wikipedia.org/wiki/Forecasting, and another good resource is the site https://otexts.com/fpp2/.
LityxIQ Forecasting Algorithms
Holt-Winters - a type of double exponential smoothing where exponential smoothing assigns exponentially decreasing weights over time unlike a simple moving average where past observations are weighted equally. http://en.wikipedia.org/wiki/Exponential_smoothing
Loess Decomposition - A filtering procedure to decompose a time series into seasonal, trend and irregular components using loess, acronym STL. http://cs.wellesley.edu/~cs315/Papers/stl%20statistical%20model.pdf
ARIMA - Autoregressive integrated moving average. A form of regression analysis that seeks to predict future movements along the seemingly random walk through examination of the differences between values in the series instead of using the actual data values. Lags of the differenced series are referred to as "autoregressive" and lags within forecasted data are referred to as "moving average." http://www.forecastingsolutions.com/arima.html
VAR - Vector Autoregression. A generalization of a univariate autoregression (AR) model. An AR model explains one variable linearly with its own previous values, while a VAR explains a vector of variables (multiple variables) with lagged values across the entire set of variables. The VAR model is a statistical tool in the sense that it finds the best regression coefficients that best describe the data available. See also https://en.wikipedia.org/wiki/Vector_autoregression.