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عنوان فارسی مقاله:
تجسم عملکرد الگوریتم پیش بینی با استفاده از سری زمانی به عنوان مثال فضاها
عنوان انگلیسی مقاله:
Visualising forecasting algorithm performance using time series instance spaces
سال انتشار : 2017
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بخشی از مقاله انگلیسی:
4. Comparison of time series forecasting methods in the instance space
The No-Free-Lunch theorem was proposed for supervised machine learning by Wolpert (1996) and for search and optimisation by Wolpert and Macready (1997). It tells us that there is never likely to be a single method that fits all situations. Similarly, there is no one time series forecasting method that will always perform best. Even for one particular time series, no one technique is consistently superior to all others (Lawrence, 2001). Petropoulos, Makridakis, Assimakopoulos, and Nikolopoulos (2014) wrote, ‘‘as there are ‘horses for courses’, there must also be forecasting methods that are more tailored to some types of data’’, and measured the extent of the effects of seven time series features on the forecasting accuracy. Smith-Miles et al. (2014) proposed a method for comparing and visualising the strengths and weaknesses of different graph colouring algorithms across an instance space. Here, we consider six general time series forecasting methods in order to demonstrate the potential of the instance space for algorithm performance visualisation. These are:abstract
It is common practice to evaluate the strength of forecasting methods using collections of well-studied time series datasets, such as the M3 data. The question is, though, how diverse and challenging are these time series, and do they enable us to study the unique strengths and weaknesses of different forecasting methods? This paper proposes a visualisation method for collections of time series that enables a time series to be represented as a point in a two-dimensional instance space. The effectiveness of different forecasting methods across this space is easy to visualise, and the diversity of the time series in an existing collection can be assessed. Noting that the diversity of the M3 dataset has been questioned, this paper also proposes a method for generating new time series with controllable characteristics in order to fill in and spread out the instance space, making our generalisations of forecasting method performances as robust as possible. © 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.Naïve: using the most recent observation as the forecast for all future periods. • Seasonal naïve: forecasts are equal to the most recent observation from the corresponding time of year. For yearly data, this is equivalent to the naïve method. • The Theta method, which performed particularly well in the M3-Competition (Assimakopoulos & Nikolopoulos, 2000; Makridakis & Hibon, 2000). We apply the method as implemented in the stheta function from the forecTheta package (Fiorucci, Louzada, & Yiqi, 2016). • ETS: exponential smoothing state space modelling (Hyndman, Koehler, Snyder, & Grose, 2002), which is used widely as a general forecasting algorithm for trended and seasonal time series. • ARIMA: autoregressive integrated moving average models, as implemented rin the automated algorithm of Hyndman and Khandakar (2008). • STL-AR: an AR model is fitted to the seasonally adjusted series obtained from a STL decomposition (Hyndman & Athanasopoulos, 2014), while the seasonal component is forecast using the seasonal naïve method. The two forecasts are summed to obtain forecasts of the original time series.
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کلمات کلیدی:
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