* Tags:: #📜Papers, [[Demand forecasting|Demand Forecasting]], [[forecasting|Forecasting]] * Authors: [[fotios-petropoulos|Fotios Petropoulos]], [[Spyros makridakis|Spyros Makridakis]], [[vassilios-assimakopoulos|Vassilios Assimakopoulos]], [[konstantinos-nikolopoulos|Konstantinos Nikolopoulos]] * Link:: https://linkinghub.elsevier.com/retrieve/pii/S0377221714001714 * Zotero Link:: [petropoulosHorsesCoursesDemand2014](zotero://select/items/@petropoulosHorsesCoursesDemand2014) * Source date:: 2014 * Finished date:: [[2021-11-01]] >even today we are unable to answer a very simple question, the one that is always the first tabled during discussions with practitioners: "what is the best method for my data?" The authors focus on six factors of a time series to compare the performance of different models: seasonality, trend, cycle, randomness, number of observations and forecasting horizon (up to 18 points ahead).. They generate time series with different levels of those factors (combined multiplicatively). It is a pity that they **only use single seasonality periods.** Results: * Single Exponential Smoothing, Damped Exponential Smoothing and the Theta method consistently perform better. * Theta method performs better for longer forecasting horizons, because it accurately predicts the trend in the data * Combinations of exponential smoothing models have very good performance. * Randomness is the variable that most affects forecasting accuracy (*qué chorprecha!*), *cycle* the next one. * A true surprise to me: >additional historical information in the form of more observations and **lengthier series improves accuracy but to a small extent**. ## Aplication in real life They build a regression model (X: the ts factors, Y: the sMAPE), and, since you can estimate the factors of a given ts (via decomposition), you may use the predicted out-of-sample sMAPE as a way to select candidate models for your forecasting (maybe directly picking an ensemble of the 6 best models, as they claim that combinations of large pools of methods is not beneficial). ![[Pasted image 20220106173042.png|400]] ## Other notes There is a interesting reference to [[Thinking fast and slow|Thinking Fast And Slow]], with the same argument [[Daniel kahneman|Daniel Kahneman]] makes again in [[Noise. a flaw in human judgement|Noise]]. >the research suggests a surprising conclusion: to maximize predictive accuracy, final decisions should be left to formulas, especially in low-validity environments The paper also includes simulations on intermittent data, that were not interesting to me at the time.