Courses taught

MBA Decision Science

Expertise 

Time series analysis and forecasting

Overview

James Taylor has an undergraduate degree in Maths from Cambridge, an MSc in Operational Research from Lancaster University, and a PhD in Decision Science from London Business School. He taught at London Business School for three years before joining Saïd Business School in 1999.

Research interests

James Taylor's research interests relate to time series analysis and forecasting. More specifically, he is interested in exponential smoothing, quantile and density forecasting, volatility forecasting, prediction intervals, and applications to energy, call centres, inventory control and finance. 

Journal articles:

Taylor, J.W. McSharry, P.E., Buizza, R. Wind Power Density Forecasting Using Wind Ensemble Predictions and Time Series Models. IEEE Transactions on Energy Conversion, Vol, 24, 2009, pp 775-782.

Little, M.A., McSharry, P.E., Taylor, J.W., Generalised Linear Models for Site-Specific Density Forecasting of UK Daily Rainfall. Monthly Weather Review, Vol, 137, 2009, pp 1031-1047.

Taylor, J.W., An Evaluation of Methods for Very Short Term Electricity Demand Forecasting Using Minute-by-Minute British Data. International Journal of Forecasting, Vol. 24, 2008, pp. 645-658

Taylor, J.W. Exponentially Weighted Information Criteria for Selecting Among Forecasting Models. International Journal of Forecasting, Vol. 24, 2008, pp. 513-524.

Taylor, J.W. Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall. Journal of Financial Econometrics, Vol. 6, 2008, pp. 382-406.

Taylor, J.W. Estimating Value at Risk and Expected Shortfall Using Expectiles. Journal of Financial Econometrics, Vol. 6, 2008, pp. 231-252.

Taylor, J.W. A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center. Management Science, Vol. 54, 2008, pp. 253-265.

Taylor, J.W., P. E. McSharry. Short-Term Load Forecasting Methods: An Evaluation Based on European Data. IEEE Transactions on Power Systems, Vol. 22, 2007, pp. 2213-2219.

Taylor, J.W. 'Forecasting Supermarket Sales Using Exponentially Weighted Quantile Regression'. European Journal of Operational Research, Vol. 178, 2007, 154-167.

Taylor, J.W. 'Density Forecasting for the Efficient Balancing of the Generation and Consumption of Electricity'. International Journal of Forecasting, Vol. 22, 2006, 707-724.

Taylor, J.W., Buizza, R., 'Density Forecasting for Weather Derivative Pricing', International Journal of Forecasting, Vol. 22, 2006, pp. 29-42.

Taylor, J.W., M. de Menezes, L.M., McSharry, P.E., 'A Comparison of Univariate Methods for Forecasting Electricity Demand Up to a Day Ahead', International Journal of Forecasting, Vol. 22, 2006, pp. 1-16.

Taylor, J.W, 'Generating Volatility Forecasts from Value at Risk Estimates', Management Science, Vol. 51, 2005, pp.712-725.

Taylor, J.W., ' Smooth Transition Exponential Smoothing', Journal of Forecasting, Vol.23, 2004, pp.385-394.

Taylor, J.W. & Buizza, R., 'Comparing Temperature Density Forecasts from GARCH and Atmospheric Models', Journal of Forecasting, Vol.23, 2004, pp.337-355.

Taylor, J.W., 'Volatility Forecasting with Smooth Transition Exponential Smoothing', International Journal of Forecasting, Vol.20, 2004, pp.273-286.

Taylor, J.W., 'Exponential Smoothing with a Damped Multiplicative Trend', International Journal of Forecasting, Vol.19, 2003, pp.715-725.

Taylor, J.W., 'Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing', Journal of Operational Research Society, Vol.54, 2003, pp.799-805.

Taylor, J.W., & Buizza, R., 'Using Weather Ensemble Predictions in Electricity Demand Forecasting', International Journal of Forecasting, Vol.19, 2003, pp.57-70.

Taylor, J.W., & Buizza, R., 'Neural Network Load Forecasting with Weather Ensemble Predictions', IEEE Transactions on Power Systems, Vol.17:3, 2002, pp.626-632.

Bunn, D.W., & Taylor, J.W., 'The Application of Quality Initiatives for Improving Short-term Judgemental Sales Forecasting', International Journal of Forecasting, Vol.17:2, 2001, pp.159-169.

Taylor, J.W., 'A Quantile Regression Neural Network Approach to Estimating the Conditional Density of Multiperiod Returns', Journal of Forecasting, Vol.19, 2000, pp.299-311.

M. de Menezes, L.M., Bunn, D.W. and Taylor, J.W., 'Review of Practical Guidelines for Combining Forecasts', European Journal of Operational Research, Vol.120:1, 2000, pp.190-204.

Taylor, J.W. and Majithia, S., 'Using Combined Forecasts with Changing Weights for Electricity Demand Profiling', Journal of the Operational Research Society, Vol.51, 2000, pp.72-82.

Taylor, J.W., 'A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns', Journal of Derivatives, Vol.7:1, 1999, pp.64-78.

Taylor, J.W. and Bunn, D.W., 'A Quantile Regression Approach to Generating Prediction Intervals', Management Science, Vol.45:2, 1999, pp.225-237.

Taylor, J.W., 'Evaluating Volatility and Interval Forecasts', Journal of Forecasting, Vol.18, 1999, pp.111-128.

Taylor, J.W. and Bunn, D.W., 'Investigating Improvements in the Accuracy of Prediction Intervals for Combinations of Forecasts: A Simulation Study', International Journal of Forecasting, Vol.15:3, 1999, pp.325-339.

Taylor, J.W. and Bunn, D.W., 'Combining Forecast Quantiles Using Quantile Regression: Investigating the Derived Weights, Estimator Bias and Imposing Constraints', Journal of Applied Statistics, Vol.25, 1998, pp.193-206.

Other publications:

Taylor, J.W., A. Espasa. Introduction to Special Issue on Energy Forecasting. International Journal of Forecasting, Vol. 24, 2008, pp.561-565

Taylor, J.W, 'Comments on 'Exponential Smoothing: The State of the Art - Part II' by E.S. Gardner, Jr.', International Journal of Forecasting, Vol. 22, 2006, pp. 671-672.

Taylor, J.W., 'Forecasting Weather Variable Densities for Weather Derivatives and Energy Prices', in: Bunn, D.W. (Ed.), Modeling Prices in Competitive Electricity Markets, Wiley, 2003.