Forecasting: theory and practice

Hendry, D. F., 2006. Robustifying Forecasts from Equilibrium-Correction Systems. Journal of Econometrics 135 (1-2), 399–426

2006

Cited alongside, same era.

Huard, D., Évin, G., Favre, A.-C., 2006. Bayesian copula selection. Computational Statistics & Data Analysis 51 (2), 809–822

2006

Cited alongside, same era.

Jardine, A. K. S., Lin, D., Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20 (7), 1483–1510

2006

Cited alongside, same era.

Jonung, L., Larch, M., 2006. Improving fiscal policy in the EU: the case for independent forecasts. Economic Policy 21 (47), 491–534

2006

Cited alongside, same era.

Jung, R. C., Tremayne, A., 2006. Coherent forecasting in integer time series models. International Journal of Forecasting 22 (2), 223–238

2006

Cited alongside, same era.

Kostenko, A. V., Hyndman, R. J., 2006. A note on the categorization of demand patterns. Journal of the Operational Research Society 57 (10), 1256–1257

2006

Cited alongside, same era.

Kusters, U., McCullough, B., Bell, M., 2006. Forecasting software: Past, present and future. International Journal of Forecasting 22 (3), 599–615

2006

Cited alongside, same era.

Zhang, X., Peng, Y., Zhang, C., Wang, B., 2015. Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences. Journal of Hydrology 530, 137–152

2015

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Zwijnenburg, J., 2015. Revisions of quarterly GDP in selected OECD countries. OECD Statistics Briefing July 2015 - No. 22, 1–12

2015

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Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., Zhang, L., 2016. Deep learning with differential privacy. In: Proc. of the 2016 ACM SIGSAC Conference on Computer and Communications Security (ACM CCS). pp. 308–318

2016

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Akouemo, H. N., Povinelli, R. J., 2016. Probabilistic anomaly detection in natural gas time series data. International Journal of Forecasting 32 (3), 948–956

2016

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Akram, F., Binning, A., Maih, J., 2015. Joint prediction bands for macroeconomic risk management. Tech. Rep. No 5/2016, Centre for Applied Macro- and Petroleum economics (CAMP) Working Paper Series

2016

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Arnott, R. D., Beck, N., Kalesnik, V., West, J., 2016. How can ‘smart beta’ go horribly wrong? SSRN:3040949

2016

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Asness, C. S., 2016. Invited editorial comment: The siren song of factor timing aka “smart beta timing” aka “style timing”. Journal of Portfolio Management 42 (5), 1–6

2016

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Austin, C., Kusumoto, F., 2016. The application of big data in medicine: current implications and future directions. Journal of Interventional Cardiac Electrophysiology 47 (1), 51–59

2016

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Azose, J. J., Ševčíková, H., Raftery, A. E., 2016. Probabilistic population projections with migration uncertainty. Proceedings of the National Academy of Sciences of the United States of America 113 (23), 6460–6465

2016

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Baade, R. A., Matheson, V. A., 2016. Going for the Gold: The economics of the Olympics. Journal of Economic Perspectives 30 (2), 201–18

2016

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Baker, S. R., Bloom, N., Davis, S. J., 2016. Measuring economic policy uncertainty. The Quarterly Journal of Economics 131 (4), 1593–1636

2016

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Barnichon, R., Garda, P., 2016. Forecasting unemployment across countries: The ins and outs. European Economic Review 84, 165–183

2016

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Barrow, D. K., Kourentzes, N., 2016. Distributions of forecasting errors of forecast combinations: implications for inventory management. International Journal of Production Economics 177, 24–33

2016

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Beckmann, J., Schussler, R., 2016. Forecasting exchange rates under parameter and model uncertainty. Journal of International Money and Finance 60, 267–288

2016

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Bekiros, S., Cardani, R., Paccagnini, A., Villa, S., 2016. Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs. Journal of Financial Stability 26 (C), 216–227

2016

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Bekiros, S. D., Paccagnini, A., 2016. Policy Oriented Macroeconomic Forecasting with Hybrid DGSE and Time Varying Parameter VAR Models. Journal of Forecasting 35 (7), 613–632

2016

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Bergmeir, C., Hyndman, R. J., Benítez, J. M., 2016. Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation. International Journal of Forecasting 32 (2), 303–312

2016

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Bisaglia, L., Canale, A., 2016. Bayesian nonparametric forecasting for INAR models. Computational Statistics and Data Analysis 100, 70–78

2016

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Boylan, J. E., Babai, M. Z., 2016. On the performance of overlapping and non-overlapping temporal demand aggregation approaches. International Journal of Production Economics 181, 136–144

2016

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Burgman, M. A., 2016. Trusting Judgements: How to Get the Best out of Experts. Cambridge University Press

2016

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Byrne, J. P., Korobilis, D., Ribeiro, P. J., 2016. Exchange rate predictability in a changing world. Journal of International Money and Finance 62, 1–24

2016

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Ca’ Zorzi, M., Muck, J., Rubaszek, M., 2016. Real exchange rate forecasting and PPP: This time the random walk loses. Open Economies Review 27 (3), 585–609

2016

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Canale, A., Ruggiero, M., 2016. Bayesian nonparametric forecasting of monotonic functional time series. Electronic Journal of Statistics 10 (2), 3265–3286

2016

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Carnevale, C., Finzi, G., Pisoni, E., Volta, M., 2016. Lazy learning based surrogate models for air quality planning. Environmental Modelling and Software 83, 47–57

2016

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Casals, J., Garcia-Hiernaux, A., Jerez, M., Sotoca, S., Trindade, A., 2016. State-Space Methods for Time Series Analysis: Theory, Applications and Software. Chapman-Hall / CRC Press

2016

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Cavalcante, L., Bessa, R. J., Reis, M., Browell, J., 2016. Lasso vector autoregression structures for very short-term wind power forecasting. Wind Energy 20, 657–675

2016

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Ceron, A., Curini, L., Iacus, S. M., 2016. Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. Routledge

2016

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Chae, Y. T., Horesh, R., Hwang, Y., Lee, Y. M., 2016. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy and Buildings 111, 184–194

2016

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Chang, Y., Kim, C. S., Park, J., 2016. Nonstationarity in time series of state densities. Journal of Econometrics 192 (1), 152–167

2016

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Chatziantoniou, I., Degiannakis, S., Eeckels, B., Filis, G., 2016. Forecasting tourist arrivals using origin country macroeconomics. Applied Economics 48 (27), 2571–2585

2016

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Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 785–794

2016

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Claeskens, G., Magnus, J. R., Vasnev, A. L., Wang, W., 2016. The forecast combination puzzle: A simple theoretical explanation. International Journal of Forecasting 32 (3), 754–762

2016

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Cludius, J., Hermann, H., Matthes, F. C., Graichen, V., 2014. The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications. Energy economics 44, 302–313

2016

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Daniel, K., Moskowitz, T. J., 2016. Momentum crashes. Journal of Financial Economics 122 (2), 221–247

2016

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De Iaco, S., Maggio, S., 2016. A dynamic model for age-specific fertility rates in Italy. Spatial Statistics 17, 105–120

2016

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de Nicola, F., De Pace, P., Hernandez, M. A., 2016. Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment. Energy Economics 57 (C), 28–41

2016

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de Queiroz, A. R., 2016. Stochastic hydro-thermal scheduling optimization: An overview. Renewable and Sustainable Energy Reviews 62, 382–395

2016

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Dudek, G., 2016. Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting. International Journal of Forecasting 32 (3), 1057–1060

2016

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Economou, T., Stephenson, D. B., Rougier, J. C., Neal, R. A., Mylne, K. R., 2016. On the use of Bayesian decision theory for issuing natural hazard warnings. Proceedings of the Royal Society: Mathematical, Physical, and Engineering Sciences 472 (2194), 20160295

2016

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Fiorucci, J. A., Pellegrini, T. R., Louzada, F., Petropoulos, F., Koehler, A. B., 2016. Models for optimising the theta method and their relationship to state space models. International Journal of Forecasting 32 (4), 1151–1161

2016

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Fiszeder, P., Perczak, G., 2016. Low and high prices can improve volatility forecasts during periods of turmoil. International Journal of Forecasting 32 (2), 398–410

2016

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Fortsch, S. M., Khapalova, E. A., 2016. Reducing uncertainty in demand for blood. Operations Research for Health Care 9, 16–28

2016

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Gaillard, P., Goude, Y., Nedellec, R., 2016. Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. International Journal of forecasting 32 (3), 1038–1050

2016

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Galvão, A. B., Giraitis, L., Kapetanios, G., Petrova, K., 2016. A time varying DSGE model with financial frictions. Journal of Empirical Finance 38, 690–716

2016

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Giacomini, R., Rossi, B., 2016. Model comparisons in unstable environments. International Economic Review 57 (2), 369–392

2016

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Gonçalves, R., 2015. Minimizing symmetric mean absolute percentage error (SMAPE). Cross Validated, URL: https://stats.stackexchange.com/q/145490 (version: 2016-04-15)

2016

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Guidolin, M., Guseo, R., 2016. The German energy transition: Modeling competition and substitution between nuclear power and renewable energy technologies. Renewable and Sustainable Energy Reviews 60, 1498–1504

2016

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Gunter, U., Önder, I., 2016. Forecasting city arrivals with Google Analytics. Annals Of Tourism Research 61, 199–212

2016

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Hamilton, J. D., 2016. Macroeconomic regimes and regime shifts. In: Taylor, J. B., Uhlig, H. (Eds.), Handbook of Macroeconomics. Vol. 2. Elsevier, pp. 163–201

2016

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Harvey, C. R., Liu, Y., Zhu, H., 2016. … and the cross-section of expected returns. Review of Financial Studies 29 (1), 5–68

2016

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Herbst, E., Schorfheide, F., 2016. Bayesian Estimation of DSGE Models, 1st Edition. Princeton University Press

2016

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Hong, T., Fan, S., 2016. Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting 32 (3), 914–938

2016

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Ibrahim, R., Ye, H., L’Ecuyer, P., Shen, H., 2016. Modeling and forecasting call center arrivals: A literature survey and a case study. International Journal of Forecasting 32 (3), 865–874

2016

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Jeon, J., Taylor, J., 2016. Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation. International Journal of Forecasting 32 (3), 991–1004

2016

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Kagraoka, Y., 2016. Common dynamic factors in driving commodity prices: Implications of a generalized dynamic factor model. Economic Modelling 52, 609–617

2016

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Klima, A., Thurner, P. W., Molnar, C., Schlesinger, T., Küchenhoff, H., 2016. Estimation of voter transitions based on ecological inference. AStA Advances in Statistical Analysis 2, 133–159

2016

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Kolassa, S., 2016. Evaluating predictive count data distributions in retail sales forecasting. International Journal of Forecasting 32 (3), 788–803

2016

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Kolassa, S., Siemsen, E., 2016. Demand forecasting for managers. Business Expert Press

2016

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Kourentzes, N., Petropoulos, F., 2016. Forecasting with multivariate temporal aggregation: The case of promotional modelling. International Journal of Production Economics 181, Part A, 145–153

2016

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Kück, M., Crone, S. F., Freitag, M., 2016. Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1499–1506

2016

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Lemke, C., Gabrys, B., 2010. Meta-learning for time series forecasting and forecast combination. Neurocomputing 73 (10-12), 2006–2016

2016

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Lohmann, T., Hering, A. S., Rebennack, S., 2016. Spatio-temporal hydro forecasting of multireservoir inflows for hydro-thermal scheduling. European Journal of Operational Research 255 (1), 243–258

2016

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López Cabrera, B., Schulz, F., 2016. Volatility linkages between energy and agricultural commodity prices. Energy Economics 54 (C), 190–203

2016

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López-Ruiz, A., Bergillos, R. J., Ortega-Sánchez, M., 2016. The importance of wave climate forecasting on the decision-making process for nearshore wave energy exploitation. Applied Energy 182, 191–203

2016

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Lübbers, J., Posch, P. N., 2016. Commodities’ common factor: An empirical assessment of the markets’ drivers. Journal of Commodity Markets 4 (1), 28–40

2016

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Ma, S., Fildes, R., Huang, T., 2016. Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information. European Journal of Operational Research 249 (1), 245–257

2016

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Maheu, J. M., Yang, Q., 2016. An infinite hidden Markov model for short-term interest rates. Journal of Empirical Finance 38, 202–220

2016

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Manner, H., Türk, D., Eichler, M., 2016. Modeling and forecasting multivariate electricity price spikes. Energy Economics 60, 255–265

2016

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Marczak, M., Proietti, T., 2016. Outlier detection in structural time series models: The indicator saturation approach. International Journal of Forecasting 32 (1), 180–202

2016

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McLean, R. D., Pontiff, J., 2016. Does academic research destroy return predictability? Journal of Finance 71 (1), 5–32

2016

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Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., Xin, D., Xin, R., Franklin, M. J., Zadeh, R., Zaharia, M., Talwalkar, A., 2016. MLlib: Machine Learning in Apache Spark. The Journal of Machine Learning Research 17 (1), 1235–1241

2016

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Moghaddam, A. H., Moghaddam, M. H., Esfandyari, M., 2016. Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science 21 (41), 89–93

2016

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Molnár, P., 2016. High-low range in GARCH models of stock return volatility. Applied Economics 48 (51), 4977–4991

2016

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Nikolopoulos, K. I., Babai, M. Z., Bozos, K., 2016. Forecasting supply chain sporadic demand with nearest neighbor approaches. International Journal of Production Economics 177, 139–148

2016

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Oh, D. H., Patton, A. J., 2016. High-dimensional copula-based distributions with mixed frequency data. Journal of Econometrics 193 (2), 349–366

2016

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Parag, Y., Sovacool, B. K., 2016. Electricity market design for the prosumer era. Nature Energy 1 (4), 1–6

2016

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Park, S. Y., Yun, B.-Y., Yun, C. Y., Lee, D. H., Choi, D. G., 2016. An analysis of the optimum renewable energy portfolio using the bottom–up model: Focusing on the electricity generation sector in South Korea. Renewable and Sustainable Energy Reviews 53, 319–329

2016

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Patti, E., Acquaviva, A., Jahn, M., Pramudianto, F., Tomasi, R., Rabourdin, D., Virgone, J., Macii, E., 2016. Event-Driven User-Centric Middleware for Energy-Efficient Buildings and Public Spaces. IEEE Systems Journal 10 (3), 1137–1146

2016

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Pereira, L. N., 2016. An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management 58, 13–23

2016

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Petropoulos, F., Fildes, R., Goodwin, P., 2016. Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour? European Journal of Operational Research 249 (3), 842–852

2016

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Pettenuzzo, D., Ravazzolo, F., 2016. Optimal portfolio choice under decision-based model combinations. Journal of Applied Econometrics 31 (7), 1312–1332

2016

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Pretis, F., Schneider, L., Smerdon, J., 2016. Detecting volcanic eruptions in temperature reconstructions by designed break-indicator saturation. Journal of Economic Surveys 30 (3), 403–429

2016

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Psaradellis, I., Sermpinis, G., 2016. Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices. International Journal of Forecasting 32 (4), 1268–1283

2016

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Qu, X., Kang, X., Zhang, C., Jiang, S., Ma, X., 2016. Short-term prediction of wind power based on deep long Short-Term memory. In: 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). pp. 1148–1152

2016

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Raftery, A. E., 2016. Use and communication of probabilistic forecasts. Statistical Analysis and Data Mining: The ASA Data Science Journal 9 (6), 397–410

2016

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Ramos, P., Oliveira, J. M., 2016. A procedure for identification of appropriate state space and ARIMA models based on time-series cross-validation. Algorithms 9 (4), 76

2016

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Rao, Y., McCabe, B., 2016. Real-time surveillance for abnormal events: the case of influenza outbreaks. Statistics in Medicine 35 (13), 2206–2220

2016

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Rossi, B., Sekhposyan, T., 2016. Forecast Rationality Tests in the Presence of Instabilities, with Applications to Federal Reserve and Survey Forecasts. Journal of Applied Econometrics 31 (3), 507–532

2016

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Smith, M. S., Vahey, S. P., 2016. Asymmetric forecast densities for US macroeconomic variables from a gaussian copula model of cross-sectional and serial dependence. Journal of Business & Economic Statistics 34 (3), 416–434

2016

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Taillardat, M., Mestre, O., Zamo, M., Naveau, P., 2016. Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics. Monthly Weather Review 144 (6), 2375–2393

2016

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Talavera-Llames, R. L., Pérez-Chacón, R., Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F., 2016. A nearest neighbours-based algorithm for big time series data forecasting. In: International Conference on Hybrid Artificial Intelligence Systems. Springer, pp. 174–185

2016

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Thorarinsdottir, T. L., Scheuerer, M., Heinz, C., 2016. Assessing the calibration of high-dimensional ensemble forecasts using rank histograms. Journal of Computational and Graphical Statistics 25 (1), 105–122

2016

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Todini, E., 2016. Predictive uncertainty assessment and decision making. In: Singh, V. P. (Ed.), Handbook of Applied Hydrology. McGraw Hill, New York, pp. 26.1–26.16

2016

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Ülkümen, G., Fox, C. R., Malle, B. F., 2016. Two dimensions of subjective uncertainty: Clues from natural language. Journal of Experimental Psychology: General 145 (10), 1280–1297

2016

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van der Laan, E., van Dalen, J., Rohrmoser, M., Simpson, R., 2016. Demand forecasting and order planning for humanitarian logistics: An empirical assessment. Journal of Operations Management 45, 114–122

2016

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Vaughan Williams, L., Reade, J., 2016. Prediction Markets, Social Media and Information Efficiency. Kyklos 69 (3), 518–556

2016

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Wang, H., Yeung, D.-Y., 2016. A survey on Bayesian deep learning. arXiv:1604.01662

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2016

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Wang, P., Liu, B., Hong, T., 2016. Electric load forecasting with recency effect: A big data approach. International Journal of Forecasting 32 (3), 585–597

2016

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Wang, X., Petropoulos, F., 2016. To select or to combine? The inventory performance of model and expert forecasts. International Journal of Production Research 54 (17), 5271–5282

2016

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Wasserstein, R. L., Lazar, N. A., 2016. The asa statement on p-values: Context, process, and purpose. The American Statistician 70 (2), 129–133

2016

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Williams, L. V., Reade, J. J., 2016. Forecasting elections. Journal of Forecasting 35 (4)

2016

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Xiao, Y., Han, J., 2016. Forecasting new product diffusion with agent-based models. Technological Forecasting and Social Change 105, 167–178

2016

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Xie, J., Hong, T., 2016. GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation. International Journal of Forecasting 32 (3), 1012–1016

2016

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Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I., 2016. Apache Spark: A unified engine for big data processing. Communications of the ACM 59 (11), 56–65

2016

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Zhang, W., Zou, Y., Tang, J., Ash, J., Wang, Y., 2016. Short-term prediction of vehicle waiting queue at ferry terminal based on machine learning method. Journal of Marine Science and Technology 21 (4), 729–741

2016

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Ziel, F., Steinert, R., 2016. Electricity price forecasting using sale and purchase curves: The X-Model. Energy Economics 59, 435–454

2016

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Zipf, G. K., 2016. Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology. Ravenio Books

2016

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Ahmad, M. W., Mourshed, M., Rezgui, Y., 2017. Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings 147, 77–89

2017

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Alvarado-Valencia, J., Barrero, L. H., Önkal, D., Dennerlein, J. T., 2017. Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting. International Journal of Forecasting 33 (1), 298–313

2017

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Apiletti, D., Baralis, E., Cerquitelli, T., Garza, P., Pulvirenti, F., Michiardi, P., 2017. A parallel mapreduce algorithm to efficiently support itemset mining on high dimensional data. Big Data Research 10, 53–69

2017

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Armstrong, C., 2017. Omnichannel retailing and demand planning. The Journal of Business Forecasting 35 (4), 10–15

2017

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Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., Petropoulos, F., 2017. Forecasting with temporal hierarchies. European Journal of Operational Research 262 (1), 60–74

2017

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Baecke, P., De Baets, S., Vanderheyden, K., 2017. Investigating the added value of integrating human judgement into statistical demand forecasting systems. International Journal of Production Economics 191, 85–96

2017

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Barbetta, S., Coccia, G., Moramarco, T., Brocca, L., Todini, E., 2017. The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting. Journal of Hydrology 551, 555–576

2017

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Beare, B. K., Seo, J., Seo, W., 2017. Cointegrated linear processes in Hilbert space. Journal of Time Series Analysis 38 (6), 1010–1027

2017

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Bélanger, A., Sabourin, P., 2017. Microsimulation and Population Dynamics: An Introduction to Modgen 12. Springer, Cham

2017

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Bessa, R., Möhrlen, C., Fundel, V., Siefert, M., Browell, J., Haglund El Gaidi, S., Hodge, B.-M., Cali, U., Kariniotakis, G., 2017. Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies 10 (9)

2017

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Binder, C. C., 2017. Measuring uncertainty based on rounding: New method and application to inflation expectations. Journal of Monetary Economics 90 (C), 1–12

2017

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Blei, D. M., Kucukelbir, A., McAuliffe, J. D., 2017. Variational inference: A review for statisticians. Journal of the American statistical Association 112 (518), 859–877

2017

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Bozkurt, Ö. Ö., Biricik, G., Tayşi, Z. C., 2017. Artificial neural network and SARIMA based models for power load forecasting in turkish electricity market. PloS One 12 (4), e0175915

2017

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Ca’ Zorzi, M., Kolasa, M., Rubaszek, M., 2017. Exchange rate forecasting with DSGE models. Journal of International Economics 107 (C), 127–146

2017

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Clements, M. P., 2017. Assessing macro uncertainty in real-time when data are subject to revision. Journal of Business & Economic Statistics 35 (3), 420–433

2017

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Clements, M. P., Galvão, A. B., 2017. Data revisions and real-time probabilistic forecasting of macroeconomic variables. Discussion Paper ICM-2017-01, ICMA, Henley Business School, Reading

2017

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Dantas, T. M., Cyrino Oliveira, F. L., Varela Repolho, H. M., 2017. Air transportation demand forecast through bagging holt winters methods. Journal of Air Transport Management 59, 116–123

2017

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Danti, P., Magnani, S., 2017. Effects of the load forecasts mismatch on the optimized schedule of a real small-size smart prosumer. Energy Procedia 126, 406–413

2017

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de Almeida Marques-Toledo, C., Degener, C. M., Vinhal, L., Coelho, G., Meira, W., Codeço, C. T., Teixeira, M. M., 2017. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting dengue at country and city level. PLoS Neglected Tropical Diseases 11 (7), e0005729

2017

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de Kok, S., 2017. The quest for a better forecast error metric: Measuring more than the average error. Foresight: The International Journal of Applied Forecasting 46, 36–45

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