Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models

2017 ◽  
pp. 272-279
Author(s):  
Tien-Wen Sung ◽  
Cian-Lin Tu ◽  
Pei-Wei Tsai ◽  
Jui-Fang Chang
Keyword(s):  
Time Series ◽  
Swarm Intelligence ◽  
Time Series Models ◽  
Short Term ◽  
Technology Industry ◽  
Short Term Forecasting

Time series models for short term forecasting of ozone in the eastern part of Austria

2001 ◽  
Vol 12 (2) ◽  
pp. 117-130 ◽  
Author(s):  
Gerd Bauer ◽  
Manfred Deistler ◽  
Wolfgang Scherrer
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Short Term ◽  
Short Term Forecasting

scholarly journals Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

2022 ◽  
Vol 70 (1) ◽  
pp. 695-714
Author(s):  
Jurgita Markevičiūtė ◽  
Jolita Bernatavičienė ◽  
Rūta Levulienė ◽  
Viktor Medvedev ◽  
Povilas Treigys ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Short Term ◽  
Short Term Forecasting

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.

scholarly journals Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking

2015 ◽  
Vol 31 (4) ◽  
pp. 627-647 ◽  
Author(s):  
Ángel Cuevas ◽  
Enrique M. Quilis ◽  
Antoni Espasa
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Real Time ◽  
Time Series Models ◽  
National Accounts ◽  
Spanish Economy ◽  
Short Term ◽  
Quarterly Data ◽  
New Instrument ◽  
Term Monitoring

Abstract In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.

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
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