2.3.Classification of Forecasts-Objective Causal Model g(a,b)=∑y-(a+bx i=l og=0 a=y-bx Ba ag =0 ∑xy-∑xn∑xy-∑x ab b=i i=l i=1 i=1 x-x2x2x-2x i= i=1 i=1 S,=2y-2%:S=2x- 之x:-∑%
Causal Model 2 1 ( , ) [ ( )] n i i i g a b y a bx 0 g a a y bx 1 11 1 2 2 11 1 1 n nn n ii i ii i i ii i xy nn n n xx ii i i ii i i x y y x n x y ny x S b S x x x n x nx x 0 g b 2 2 ; () n nn n n xy i i i i xx i i i ii i i S n xy x y S n x x 1 1 ; ; n n i i i i x xy y n n 2.3. Classification of Forecasts - Objective
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 data
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 data. 2.3. Classification of Forecasts - Objective
2.3.Classification of Forecasts -Objective Purely random- Increasing No recognizable linear trend pattern ● ● ● ● ● ● Time Time Curvilinear Seasonal trend (quadratic, pattern plus exponential) linear growth ● ● pupwe Time Time Fig.2-2 Time Series Patterns
Fig. 2-2 Time Series Patterns 2.3. Classification of Forecasts - Objective
@ Chapter 2 Forecasting Contents 1.Introduction 2.The Time Horizon in Forecasting 3.Classification of Forecasts 4.Evaluating Forecast 5.Notation Conventions 6.Methods for Forecasting Stationary Series 7.Trend-Based Methods 8.Methods for Seasonal Series
Chapter 2 Forecasting Contents 1. Introduction 2. The Time Horizon in Forecasting 3. Classification of Forecasts 4. Evaluating Forecast 5. Notation Conventions 6. Methods for Forecasting Stationary Series 7. Trend-Based Methods 8. Methods for Seasonal Series