46,727 research outputs found
Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models
This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively
Application of Machine Learning to Mortality Modeling and Forecasting
Estimation of future mortality rates still plays a central role among life insurers in
pricing their products and managing longevity risk. In the literature on mortality modeling, a wide
number of stochastic models have been proposed, most of them forecasting future mortality
rates by extrapolating one or more latent factors. The abundance of proposed models shows that
forecasting future mortality from historical trends is non-trivial. Following the idea proposed in
Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly
identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit
of standard stochastic mortality models. The machine learning estimator is then forecasted according
to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard
stochastic models. Out-of sample forecasts are provided to verify the model accuracy
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