UK Retail market share dynamics using ARIMA

David Horgan

In October 2018 I used ARIMA to forecast UK retail market share in 2019 using data from 2015 through to October 2018. That work can be found here: This post updates and verifies these forecasts using Kandar data from October 2018 through to July 2019.

In order to predict the UK retail market share, I used the Python Statsmodel package: statsmodels.tsa.arima_model. To fit the historical time series data. The optimum ARIMA(p,d,q) forecasting equation was selected using hypergrid parameter selection. The simple exponential smoothing value, ARIMA (0,1,1), was the best performing model by Akaike’s Information Criterion (AIC).

In order to evaluate the fitting and forecasting performance of the ARIMA model, I used the mean square error (MSE) and root mean square error (RMSE).

UK Retail market share 2015–2018

The forecasting performance of this model is quite satisfactory over the validation period of 1-year with the actual marketshare falling solidly within the 95% confidence level.

The Github repository for this project is here.

David Horgan

Written by

I am a theoretical physicist with a data science background. At present, I am developing a UK retail market using ABM, ML and computational econometrics.

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