James W. Taylor

 

James W. Taylor
Sad Business School
University of Oxford
Park End Street
Oxford OX1 1HP
United Kingdom
email: james.taylor@sbs.ox.ac.uk

 


Research interests:

    My research is in time series forecasting. I am particularly interested in two methodological areas: (1) the estimation of forecast uncertainty and (2) exponential smoothing methods.

     

    My work has mainly concerned the following application areas: call centres, energy, financial markets and inventory control.


Published and Accepted Journal Papers:

    Arora, S., Taylor, J.W. Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods. IEEE Transactions on Power Systems, forthcoming. (pdf)

    Jeon, J., Taylor, J.W. 2013. Using Implied Volatility with CAViaR Models for Value at Risk Estimation. Journal of Forecasting, 32, 62-74. (pdf)

    Jeon, J., Taylor, J.W. 2012. Using Conditional Kernel Density Estimation for Wind Power Density Forecasting. Journal of the American Statistical Association, 107, 66-79. (pdf)

    Taylor, J.W. 2012. Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing. Management Science, 58, 534-549. (pdf)

    Taylor, J.W. 2012. Short-Term Load Forecasting with Exponentially Weighted Methods. IEEE Transactions on Power Systems, 27, 458-464. (pdf)

    Taylor, J.W., Snyder, R.D. 2012. Forecasting Intraday Data with Multiple Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing. Omega, 40, 748-757. (pdf)

    Taylor, J.W. 2011. Multi-item Sales Forecasting with Total and Split Exponential Smoothing. Journal of the Operational Research Society, 62, 555563. (pdf)

    Taylor, J.W. 2010. Exponentially Weighted Methods for Forecasting Intraday Time Series with Multiple Seasonal Cycles. International Journal of Forecasting, 26, 627-646. (pdf)

    Hippert, H., Taylor, J.W. 2010. An Evaluation of Bayesian Techniques for Controlling Model Complexity in a Neural Network for Short-term Load Forecasting, Neural Networks, 23, 386-395. (pdf)

    Taylor, J.W. 2010. Triple Seasonal Methods for Short-term Load Forecasting. European Journal of Operational Research, 204, 139-152. (pdf)

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

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

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

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

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

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

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

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

    Taylor, J.W. 2007. Forecasting Daily Supermarket Sales Using Exponentially Weighted Quantile Regression. European Journal of Operational Research, 178, 154-167. (pdf)

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

    Taylor, J.W., R. Buizza. 2006. Density Forecasting for Weather Derivative Pricing. International Journal of Forecasting, 22, 29-42. (pdf)

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

    Taylor, J.W. 2005. Generating Volatility Forecasts from Value at Risk Estimates. Management Science, 51, 712-725. (pdf)

    Taylor, J.W. 2004. Smooth Transition Exponential Smoothing. Journal of Forecasting, 23, 385-394. (pdf)

    Taylor, J.W., R. Buizza. 2004. A Comparison of Temperature Density Forecasts from GARCH and Atmospheric Models. Journal of Forecasting, 23, 337-355. (pdf)

    Taylor, J.W. 2004. Volatility Forecasting with Smooth Transition Exponential Smoothing. International Journal of Forecasting, 20, 273-286. (pdf)

    Taylor, J.W. 2003. Exponential Smoothing with a Damped Multiplicative Trend. International Journal of Forecasting, 19, 715-725. (pdf)

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

    Taylor, J.W., R. Buizza. 2003. Using Weather Ensemble Predictions in Electricity Demand Forecasting. International Journal of Forecasting, 19, 57-70. (pdf)

    Taylor, J.W., R. Buizza. 2002. Neural Network Load Forecasting with Weather Ensemble Predictions. IEEE Transactions on Power Systems, 17, 626-632. (pdf)

    Bunn, D.W., J.W. Taylor. 2001. The Application of Quality Initiatives for Improving Short-term Judgemental Sales Forecasting. International Journal of Forecasting, 17, 159-169. (pdf)

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

    M. de Menezes, L.M., D.W. Bunn, J.W. Taylor. 2000. Review of Practical Guidelines for Combining Forecasts. European Journal of Operational Research, 120, 190-204. (pdf)

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

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

    Taylor, J.W., D.W. Bunn. 1999. A Quantile Regression Approach to Generating Prediction Intervals. Management Science, 45, 225-237.

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

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

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


Other publications:

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

     

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

     

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


Teaching:


My links:


This page was last updated 31/07/2013 16:22