2. Time series data: each case represents a point in time. Each cell gives a value for each variable for each time period. Stationarity: Data are stationary. A stationary process has the property that the mean, variance and autocorrelation structure do not change over time. Seasonality: By seasonality, we mean periodic fluctuations. The usage of time series models is: to obtain an understanding of underlying forces and structures that produce the observed data. to fit a model and proceed to forecasting and monitoring. Techniques: Exponential Smoothing ARIMA Models

3. Exponential smooting Exponential Smoothing Four available model types: Simple. The simple model assumes that the series has no tr end and no seasonal variation. Holt. The Holt model assumes that the series has a linear trend and no seasonal variation. Winters. The Winters model assumes that the series has a li near trend and multiplicative seasonal variation (its magnitude incre ases or decreases with the overall level of the series). Custom. A custom model allows you to specify the trend an d seasonality components.