James W. Taylor
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James W. Taylor |
|
Park End Street |
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Oxford OX1 1HP |
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United Kingdom |
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email:
james.taylor@sbs.ox.ac.uk |
My
research is in time series forecasting. I am particularly interested in various
aspects of probabilistic forecasting.
My
work has mainly concerned the following application areas: call centres, energy
and the environment, financial markets and inventory management.
Published and Accepted Journal Papers:
Meng,
X., Taylor, J.W., Ben Taieb, S., Li, S. Scores for Multivariate Distributions
and Level Sets, Operations Research, forthcoming. (pdf)
Arora,
S., Taylor, J.W., Mak, H.-Y. Probabilistic Forecasting of Patient Waiting Times
in an Emergency Department, Manufacturing & Service Operations
Management, forthcoming. (pdf)
Taylor,
J.W. Forecasting Value at Risk and Expected Shortfall using a Model with a
Dynamic Omega Ratio, Journal of Banking & Finance, forthcoming. (pdf)
Trucíos,
C., Taylor, J.W. A Comparison of Methods for Forecasting Value-at-Risk and
Expected Shortfall of Cryptocurrencies, Journal of Forecasting, forthcoming.
(pdf)
Ludwig,
N., Arora, S., Taylor, J.W. Modelling Uncertainty: Probabilistic Forecasting
Using Weather Ensemble Predictions, Journal of the Operational Research
Society, forthcoming. (pdf)
Taylor,
J.W., Taylor, K.S. 2023. Combining Probabilistic Forecasts of U.S. COVID-19
Mortality. European Journal of Operational Research, 304(1), 25-41. (pdf)
Taylor,
K.S., Taylor, J.W. 2022. Interval forecasts of weekly incident and cumulative
COVID-19 mortality in the United States: A comparison of combining methods, PLOS One, 17(3), e0266096. (pdf) (Supplement: pdf)
Meng,
X., Taylor, J.W. 2022. Comparing Probabilistic Forecasts of the Daily Minimum
and Maximum Temperature. International Journal of Forecasting, 38(1),
267-281. (pdf)
Ben
Taieb, S., Taylor, J.W., Hyndman, R.J. 2021. Hierarchical Probabilistic
Forecasting of Electricity Demand with Smart Meter Data. Journal of the
American Statistical Association, 116(533), 27-43. (pdf)
Taylor,
J.W. 2021. Evaluating Quantile-Bounded and Expectile-Bounded Interval
Forecasts. International Journal of Forecasting, 37(2), 800-811. (pdf)
Liu,
M., Taylor, J.W., Choo, W.-C. 2020. Further Empirical Evidence on the
Forecasting of Volatility with Smooth Transition Exponential Smoothing. Economic
Modelling, 93, 651-659. (pdf)
Taylor, J.W. 2020. A Strategic Predictive Distribution for Tests of
Probabilistic Calibration. International Journal of Forecasting, 36(4),
1380-1388. (pdf)
Taylor, J.W. 2020. Forecast Combinations for Value at Risk and Expected
Shortfall. International Journal of Forecasting, 36(2), 428-441. (pdf)
Meng,
X., Taylor, J.W. 2020. Estimating Value-at-Risk and Expected Shortfall Using
the Intraday Low and Range. European Journal of Operational Research,
280(1), 191-202. (pdf)
Taylor, J.W. 2019. Forecasting Value at Risk and Expected Shortfall Using a
Semiparametric Approach Based on the Asymmetric Laplace Distribution. Journal
of Business and Economic Statistics, 37(1), 121-133. (pdf)
Taylor, J.W., Jeon, J. 2018. Probabilistic Forecasting of Wave Height for
Offshore Wind Turbine Maintenance, European Journal of Operational Research,
267(3), 877-890. (pdf)
Meng, X., Taylor, J.W. 2018. An Approximate Long-Memory Range-Based Approach
for Value at Risk Estimation, International Journal of Forecasting,
34(3), 377-388. (pdf)
Arora, S., Taylor, J.W. 2018. Rule-based Autoregressive Moving Average Models
for Forecasting Load on Special Days: A Case Study for France, European
Journal of Operational Research, 266(1), 259-268. (pdf)
Taylor, J.W. 2017. Probabilistic Forecasting of Wind Power Ramp Events Using
Autoregressive Logit Models. European Journal of Operational Research,
259(2), 703-712. (pdf)
Taylor, J.W., Yu, K. 2016. Using Autoregressive Logit Models to Forecast the
Exceedance Probability for Financial Risk Management. Journal of the Royal
Statistical Society, Series A, 179(4), 1069-1092. (pdf)
Jeon, J., Taylor, J.W. 2016. Short-term Density Forecasting of Wave Energy
Using ARMA-GARCH Models and Kernel Density Estimation. International Journal
of Forecasting, 32(3), 991-1004. (pdf)
Taylor, J.W., Roberts, M.B. 2016. Forecasting Frequency-Corrected Electricity
Demand to Support Frequency Control. IEEE Transactions on Power Systems,
31(3), 1925-1932. (pdf)
Arora, S., Taylor, J.W. 2016. Forecasting Electricity Smart Meter Data using
Conditional Kernel Density Estimation. Omega, 59, 47-59. (pdf)
Taylor, J.W., Jeon, J. 2015. Forecasting Wind Power Quantiles Using Conditional
Kernel Estimation. Renewable Energy, 80, 370-379. (pdf)
Arora, S., Taylor, J.W. 2013. Short-term
Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods. IEEE
Transactions on Power Systems, 28(3), 3235-3242. (pdf)
Jeon, J., Taylor, J.W. 2013. Using Implied
Volatility with CAViaR Models for Value at Risk Estimation. Journal of
Forecasting, 32(1), 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(497), 66-79. (pdf)
Taylor, J.W. 2012. Density Forecasting of Intraday Call Center Arrivals Using
Models Based on Exponential Smoothing. Management Science, 58(3),
534-549. (pdf)
Taylor, J.W. 2012. Short-Term Load Forecasting with Exponentially Weighted Methods.
IEEE Transactions on Power Systems, 27(1), 458-464. (pdf)
Taylor, J.W., Snyder, R.D. 2012. Forecasting Intraday Data with Multiple
Seasonal Cycles Using Parsimonious Seasonal Exponential Smoothing. Omega,
40(6), 748-757. (pdf)
Taylor, J.W. 2011. Multi-item Sales Forecasting with Total and Split
Exponential Smoothing. Journal of the Operational Research Society,
62(3), 555-563. (pdf)
Taylor, J.W. 2010. Exponentially Weighted Methods for Forecasting Intraday Time
Series with Multiple Seasonal Cycles. International Journal of Forecasting,
26(4), 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(3), 386-395. (pdf)
Taylor, J.W. 2010. Triple Seasonal Methods for Short-term Load Forecasting. European
Journal of Operational Research, 204(1), 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(3), 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(3), 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(4), 645-658. (pdf)
Taylor, J.W. 2008. Exponentially Weighted Information Criteria for Selecting
Among Forecasting Models. International Journal of Forecasting, 24(3),
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(3), 382-406. (pdf)
Taylor, J.W. 2008. Estimating Value at Risk and Expected Shortfall Using
Expectiles. Journal of Financial Econometrics, 6(2), 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(2), 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(4), 2213-2219. (pdf)
Taylor, J.W. 2007. Forecasting Daily Supermarket Sales Using Exponentially
Weighted Quantile Regression. European Journal of Operational Research,
178(1), 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(4), 707-724. (pdf)
Taylor, J.W., R. Buizza. 2006. Density Forecasting for Weather Derivative
Pricing. International Journal of Forecasting, 22(1), 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),1-16. (pdf)
Taylor, J.W. 2005. Generating Volatility Forecasts from Value at Risk
Estimates. Management Science, 51(5), 712-725. (pdf)
Taylor, J.W. 2004. Smooth Transition Exponential Smoothing. Journal of
Forecasting, 23(6), 385-394. (pdf)
Taylor, J.W., R. Buizza. 2004. A Comparison of Temperature Density Forecasts
from GARCH and Atmospheric Models. Journal of Forecasting, 23(5),
337-355. (pdf)
Taylor, J.W. 2004. Volatility Forecasting with Smooth Transition Exponential
Smoothing. International Journal of Forecasting, 20(2), 273-286. (pdf)
Taylor, J.W. 2003. Exponential Smoothing with a Damped Multiplicative Trend. International
Journal of Forecasting, 19(4), 715-725. (pdf)
Taylor, J.W. 2003. Short-Term Electricity Demand Forecasting Using Double
Seasonal Exponential Smoothing. Journal of Operational Research Society,
54(8), 799-805. (pdf)
Taylor, J.W., R. Buizza. 2003. Using Weather Ensemble Predictions in
Electricity Demand Forecasting. International Journal of Forecasting,
19(1), 57-70. (pdf)
Taylor, J.W., R. Buizza. 2002. Neural Network Load Forecasting with Weather
Ensemble Predictions. IEEE Transactions on Power Systems, 17(3),
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(2), 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(4), 299-311. (pdf)
M. de Menezes, L.M., D.W. Bunn, J.W. Taylor. 2000. Review Guidelines for
Combining Forecasts. European Journal of Operational Research, 120(1),
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(1), 72-82. (pdf)
Taylor, J.W. 1999. A Quantile Regression Approach to Estimating the
Distribution of Multiperiod Returns. Journal of Derivatives, 7(1),
64-78.
Taylor, J.W., D.W. Bunn. 1999. A Quantile Regression Approach to Generating Prediction
Intervals. Management Science, 45(2), 225-237.
Taylor, J.W. 1999. Evaluating Volatility and Interval Forecasts. Journal of
Forecasting, 18(2), 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(3), 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(2), 193-206.
Other publications:
Ben Taieb, S., Taylor, J.W., Hyndman,
R.J. Coherent Probabilistic Forecasts for Hierarchical Time Series. Proceedings
of the 34th International Conference on Machine Learning,
Sydney, Australia, 2017. (pdf)
Taylor, J.W., A. Espasa. 2008.
Introduction to Special Issue on Energy Forecasting. International Journal
of Forecasting, 24(4), 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(4), 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.
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page was last updated 19/12/2017 14:37