Time Series Performance and Limitations with SARIMAX: An Application with Retail Store Data
Emre S. ÖZMEN · Journal of Turkish Studies · 2021
World giant retailers’ sales data competes with stock exchange data in respect to latency, where the number one reason is the number of transactions, it is around few hundreds per second and it only goes up in time being. This emerges the idea of making an ideal application area for time series, however the field looks like lacking comparisons. This is an attempt to address its dynamics with a generic reference data that was published with Walmart retail figures. Time series has a long list of predictive models, however they are all based on regression and the problem is that regression is not always make the leader model. Here we explored the possible performance bottlenecks with most popular techniques, like ARIMA/SARIMA derivative and try to answer if regression limits time series foundations. Does the best fit with ARIMA derivatives always give the best scores? In other words, per time-series performance, does this make a lagging or leading factor? This situation created two new question groups for future studies. The first was concerned with the possibility that time series, which have not yet been exemplified in the world, are based on classification models rather than regression. In other words, the possible positive effect of fuzzy logic applications on performance, which matches the size of the classification models that produce output in binary number order with the size of turnover in pre-output estimates based on probability percentages. The second was the fact that time series, which by definition do not accept data other than time, will not produce an important factor, and to what extent this effort might be needed.