We propose a simple way of predicting time series with reoccurring seasonal periods. We combine several forecasting methods by taking the samplewise weighted mean of those forecasts that were generated with models showing low prediction errors on left-out parts of the time-series. We show the application of this approach to the NN5 Time Series Competition data set.

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Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability respect the forecasts of loads and generation renewable energies. However, while there are lots approaches accounting uncertainties in these forecasts, their impact rarely analyzed systematically. Here, we use a simplified linear state space model commercial building including photovoltaic (PV) pl...

We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.

Forecasting in ation is fundamental to UK monetary policy, both for policy-makers and private agents. However, forecast failure is prevalent with naive devices often outperforming the dominant congruent in-sample model in forecasting competitions. This paper assesses evidence for UK annual and quarterly in ation using the theoretical framework developed by Clements and Hendry (1998, 1999) to ex...

G13AFF is an easy-to-use version of G13AEF. It fits a seasonal autoregressive integrated moving average (ARIMA) model to an observed time series, using a nonlinear least-squares procedure incorporating backforecasting. Parameter estimates are obtained, together with appropriate standard errors. The residual series is returned, and information for use in forecasting the time series is produced f...

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recu...

Abstract: The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore,...