scholarly journals Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns

2018 ◽  
Vol 132 ◽  
pp. 1832-1841 ◽  
Author(s):  
Iram Naim ◽  
Tripti Mahara ◽  
Ashraf Rahman Idrisi
Keyword(s):  
Time Series ◽  
Seasonal Patterns ◽  
Short Term ◽  
Daily Time Series ◽  
Short Term Forecasting ◽  
Daily Time

scholarly journals A New Multipollutant, No-Threshold Air Quality Health Index Based on Short-Term Associations Observed in Daily Time-Series Analyses

2008 ◽  
Vol 58 (3) ◽  
pp. 435-450 ◽  
Author(s):  
David M. Stieb ◽  
Richard T. Burnett ◽  
Marc Smith-Doiron ◽  
Orly Brion ◽  
Hwashin Hyun Shin ◽  
...  
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Health Index ◽  
Short Term ◽  
Daily Time Series ◽  
Quality Health ◽  
Time Series Analyses ◽  
Air Quality Health Index ◽  
Daily Time

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

Homogenization of daily time series climatological data in the Eastern Nile basin, Ethiopia

2020 ◽  
Vol 143 (1-2) ◽  
pp. 737-760
Author(s):  
Sadame M. Yimer ◽  
Navneet Kumar ◽  
Abderrazak Bouanani ◽  
Bernhard Tischbein ◽  
Christian Borgemeister
Keyword(s):  
Time Series ◽  
Nile Basin ◽  
Climatological Data ◽  
Daily Time Series ◽  
Daily Time

scholarly journals CO and CO2 daily time series and time series of wind speed

Data in Brief ◽  
2019 ◽  
Vol 24 ◽  
pp. 103976
Author(s):  
Changgong Shan ◽  
Wei Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Daily Time Series ◽  
Daily Time
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