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Model Selection Techniques Model Selection We are primarily concerned with prediction accuracy versus model interpretability. In some cases, we may have to trade-o↵ one for the other; in other cases, we can achieve both objectives. Copyright By PowCoder代写 加微信 powcoder 1. Prediction Accuracy If the true relationship is linear, then OLS will have low bias;

ETC3231/5231 Business forecasting Ch8. Exponential smoothing OTexts.org/fpp3/ Outline 1 Exponential smoothing 2 Simple exponential smoothing 3 Models with trend 4 Models with seasonality 5 Innovations state space models 6 Forecasting with exponential smoothing 2 Outline 1 Exponential smoothing 2 Simple exponential smoothing 3 Models with trend 4 Models with seasonality 5 Innovations state space models

ETF3231/5231: Business forecasting Ch3. Time series decomposition OTexts.org/fpp3/ Outline 1 Transformations and adjustments 2 Time series components 3 Moving averages 4 Classical decomposition 5 History of time series decomposition 6 STL decomposition 2 Outline 1 Transformations and adjustments 2 Time series components 3 Moving averages 4 Classical decomposition 5 History of time series decomposition 6

ETC3231/5231 Business forecasting Ch8. Exponential smoothing OTexts.org/fpp3/ Outline 1 Exponential smoothing 2 Simple exponential smoothing 3 Models with trend 4 Models with seasonality 5 Innovations state space models 6 Forecasting with exponential smoothing 2 Outline 1 Exponential smoothing 2 Simple exponential smoothing 3 Models with trend 4 Models with seasonality 5 Innovations state space models

2021/6/6 https://lms.monash.edu/pluginfile.php/12413905/mod_resource/content/0/3-decomposition.R library(fpp3) ## GDP ————————————————————————– global_economy %>% filter(Country == “Australia”) %>% autoplot(GDP) global_economy %>% filter(Country == “Australia”) %>% autoplot(GDP / Population) ## Print retail adjusted by CPI ————————————————– print_retail % filter(Industry == “Newspaper and book retailing”) %>% group_by(Industry) %>% # Just to keep the key in there index_by(Year = year(Month)) %>% summarise(Turnover = sum(Turnover))