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Forecasting in business is a vital activity for planning and strategizing, one that occurs in virtually every area, including marketing (Makridakis and Wheelwright, 1977), operations (Fildes et al., 2008), HRM (Malik, 2018; Turner, 2002), startups (Hyytinen, Lahtoneni and Pajarinen, 2014), diffusion of innovations (Meade and Islam, 2006), finance (Lin, Wu and Zhou, 2018) and business profitability (Ding, Zhang and Duygun, 2019).

Sissejuhatavad soovitused:

In making use of forecasting in business and management research, key issues to be considered are the variable(s) to be predicted, the accuracy required, the horizon and timing of the forecast and, more importantly, the data on which the forecast is based (Makridakis and Wheelwright, 1977; Ren et al., 2020). Accuracy is a key dimension of a forecast […]

Autorid lubavad:

we present a novel forecasting model to be used with short time series with limited data, which we correspondingly term ShoTS, for short time series forecasting. This model could therefore be used when observations on a variable of interest are relatively few, yet where there is value in deriving a reasonable forecast.


There are two broad types of forecasting approaches. Qualitative approaches generally base a prediction on the views of a panel of experts. Quantitative approaches make use of data gathered to forecast a future quantity or quantities of interest (for a thorough review of various forecasting technics, see Petropoulos et al., 2022).


One forecasting model with which researchers have had some success using small databases is the ‘grey’ model, 1 which is based on grey system theory and was originally developed by Deng (1982). The grey model is specifically designed to be used when data are limited or there are missing data points (Askari and Askari, 2011).

Lugemishuvi suurendamiseks:

The ShoTS model has a relatively simple technical foundation, that is the most basic benchmark, the naïve forecast. […] Our approach has applicability not only to instances where there is a shortage of data points but, indeed, to non-stationary-type time series, which are notoriously more difficult to predict than those of stationary type. […] More specifically, as the sample size increases, the forecasting rule will collapse to the intuitive beginning of our method, that is the naïve forecast; this contrasts with other methods so far, whose design is either sample-size agnostic or that require a large sample size to perform well.

Thomakos, D., Wood, G., Ioakimidis, M., & Papagiannakis, G. (2022). ShoTS Forecasting: Short Time Series Forecasting for Management ResearchBritish Journal of Management.