Long-term forecasting also remains an important issue, especially for policy decision for better control of air pollution and for evaluation of the long term impacts on public health. Given the well-recognized negative effects of PM2.5, NO2 and O3 on public heath, we study their time series ...
Taken at face value, we may conclude that LSTMs are unsuitable for AR-based univariate time series forecasting. That we should turn first to MLPs with a fixed window and only to LSTMs if MLPs cannot achieve a good result. This sounds fair. I would argue a few points that should be con...
This work focuses on forecasting traffic flow in major urban areas and freeways in the state of Georgia using large amounts of data collected from traffic sensors. Much of the existing work on traffic flow forecasting focuses on the immediate short terms. In addition to that, this work studies...
short-termloadtimeseriesforecastingbasedoncorrelativeneighboringpoints 系统标签: 峰谷邻近负荷预测序列correlative 基于相关邻近点与峰谷荷修正的短期负荷时间序列预测 唐 巍!谷 子 <中国农业大学信息与电气工程学院9北京市 100083 > 摘要!采用混沈相空间重构理论进行电力短期负荷预测!存在峰谷荷预测精度相对较差和预测参...
Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1week. The pro......
Short-Term Price Forecasting For Agro-products Using Artificial Neural Networks It is well known that short-term market price forecasting has been a difficult problem for a long time because of too many factors which can not be accurat... GQ Li,SW Xu,ZM Li - 《Agriculture & Agricultural Sc...
Analysis and forecasting for short-term traffic flow have become a critical problem in intelligent transportation system (ITS). This paper introduces the basic theory and features of General Regression Neural Network (GRNN) and its advantages. A forecast
H. L. Shang (2012) "Functional time series approach for forecasting very short-term electricity demand", Journal of Applied Statistics, in press.H. L. Shang. Functional time series approach for fore- casting very short-term electricity demand. Journal of Applied Statistics, in press, 2012b. ...
As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. I...
It is based on the concept of modifying the local curvatures of the time series, obtained by a theta () coefficient. The central idea is to decompose the time series into at least two theta lines L() representing a long term period and the other, a short-term one. The Forecast...