Forecasting for big data: Does suboptimality matter?
By Konstantinos Nikolopoulos, Fotios Petropoulos. 2017
48 citations recorded by the source corpus.
Traditionally, forecasters focus on the development algorithms to identify optimal models and sets of parameters, optimal in the sense of within-sample fitting. However, this quest strongly assumes that optimally set parameters will also give the best extrapolations. The problem becomes even more pertinent when we consider the vast volumes of data to be forecast in the big data era. In this paper, we argue if this obsession to optimality always bares the respective fruits or do we spend too much time and effort in the pursuit of it. Could we better off by targeting for faster and robust systems that would aim for suboptimal forecasting solutions which, in turn, would not jeopardise the efficiency of the systems under use? This study throws light to that end by means of an empirical investigation. We show the trade-off between optimal versus suboptimal solutions in terms of forecasting performance versus computational cost. Finally, we discuss the implications of suboptimality and attempt to quantify the monetary savings as a result of suboptimal solutions.
Subfields: Economics and Econometrics, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Forecasting: theory and practice (2020, background): “, 2020c), especially when forecasting numerous series at the weekly and daily frequency (Seaman, 2018) or long-term accuracy improvements over traditional methods are insignificant (Nikolopoulos and Petropoulos, 2018).”
- Big Data Analytics in Operations Management (2018, background): “See Nikolopoulos and Petropoulos (2018) for a recent study.”
- A simple combination of univariate models (2020, background): “In fact, it is argued that any added benefits in accuracy when comparing approach A with approach B (the benchmark) should be balanced against the additional computational time that is needed to compute the forecasts for A compared to B (Nikolopoulos and Petropoulos, 2018).”