MAE Criteria Research Articles

Univariate forecasting of day-ahead hourly electricity demand in the northern grid of India

Short-term electricity demand forecasts (minutes to several hours ahead) have become increasingly important since the rise of the competitive energy markets. The issue is particularly important for India as it has recently set up a power exchange (PX), which has been operating on day-ahead hourly basis. In this study, an attempt has been made to forecast day-ahead hourly demand of electricity in the northern grid of India using univariate time-series forecasting techniques namely multiplicative seasonal ARIMA and Holt-Winters multiplicative exponential smoothing (ES). In-sample forecasts reveal that ARIMA models, except in one case, outperform ES models in terms of lower RMSE, MAE and MAPE criteria. We may conclude that linear time-series models works well to explain day-ahead hourly demand forecasts in the northern grid of India. The findings of the study will immensely help the players in the upcoming power market in India.

Design of optimal stack filters under the MAE criterion

The design of optimal stack filters under the MAE criterion is addressed in this paper. In our work, the Hasse diagram is adopted to represent the positive Boolean functions to solve the optimization problem. After problem transformation, the finding of the optimal stack filter is equivalent to the finding of the optimal on-set such that the total cost of the on-set is minimal. An efficient algorithm is developed that makes use of an important property of the optimal on-set to avoid fruitless search. It thereby greatly reduces the complexity in finding the corresponding optimal stack filter. A design example is illustrated in detail to demonstrate the optimization procedures. The proposed algorithm can generate the optimal stack filter in 1 s for the window size of 14 pixels. It can still generate the optimal stack filter for the window size of 21, although it takes about 4 h. Experimental results for real images reveal that the proposed algorithm essentially extends the maximum filter window size to make the stack filter optimization problem computationally tractable.

Shape preservation criteria and optimal soft morphological filtering

New criteria for shape preservation are presented. These criteria are applied in optimizing soft morphological filters. The filters are optimized by simulated annealing and genetic algorithms which are briefly reviewed. Situations, where this kind of criteria give better results compared to the traditional MAE and MSE criteria, are illustrated.

Are the GARCH models best in out-of-sample performance?

Abstract Out-of-sample performance of exchange rate volatility model depends on criteria used to measure it. The linear GARCH models cannot generally outperform the nonlinear models in the RMSE criterion. Furthermore, the nonparametric kernel model is best in the MAE criterion.