2.3.Classification of Forecasts -Objective Time Series Methods The idea is that information can be inferred from the pattern of past observations and can be used to forecast future values of the series. Try to isolate the following patterns that arise most often. -Trend-the tendency of a time series,usually a stable growth or decline,either linear (a line)or nonlinear(described as nonlinear function,e.g.a quadratic or exponential curve) -Seasonality-Variation of a series related to seasonal changes and repeated every season. -Cycles-Cyclic variation similar to seasonality,except that the length and the magnitude may change,usually associated with economic variation. Randomness-No recognizable pattern to the dataTime Series Methods • The idea is that information can be inferred from the pattern of past observations and can be used to forecast future values of the series. • Try to isolate the following patterns that arise most often. Trend-the tendency of a time series, usually a stable growth or decline, either linear (a line) or nonlinear (described as nonlinear function, e. g. a quadratic or exponential curve) Seasonality-Variation of a series related to seasonal changes and repeated every season. Cycles-Cyclic variation similar to seasonality, except that the length and the magnitude may change, usually associated with economic variation. Randomness-No recognizable pattern to the data. 2.3. Classification of Forecasts - Objective