scholarly journals A Bayesian Time‐Varying Autoregressive Model for Improved Short‐ and Long‐Term Prediction

2021 ◽  
Author(s):  
Christoph Berninger ◽  
Almond Stöcker ◽  
David Rügamer
Keyword(s):  
Autoregressive Model ◽  
Time Varying ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

scholarly journals Serotonin Differentially Regulates Short- and Long-Term Prediction of Rewards in the Ventral and Dorsal Striatum

PLoS ONE ◽  
2007 ◽  
Vol 2 (12) ◽  
pp. e1333 ◽  
Author(s):  
Saori C. Tanaka ◽  
Nicolas Schweighofer ◽  
Shuji Asahi ◽  
Kazuhiro Shishida ◽  
Yasumasa Okamoto ◽  
...  
Keyword(s):  
Dorsal Striatum ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

O4-04-05: Differences in short- and long-term prediction of dementia in a 15-year longitudinal population study

2006 ◽  
Vol 2 ◽  
pp. S83-S83
Author(s):  
Simona F. Sacuiu ◽  
Boo Johansson ◽  
Svante Östling ◽  
Deborah Gustafson ◽  
Ingmar Skoog
Keyword(s):  
Population Study ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

scholarly journals DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

2020 ◽  
Vol 10 (4) ◽  
pp. 1504 ◽  
Author(s):  
Imam Mustafa Kamal ◽  
Hyerim Bae ◽  
Sim Sunghyun ◽  
Heesung Yun
Keyword(s):  
Deep Learning ◽  
Network Models ◽  
Neural Network Models ◽  
Business Decisions ◽  
Term Prediction ◽  
Short And Long Term ◽  
The Baltic ◽  
Long Term Prediction ◽  
Baltic Dry Index

The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

scholarly journals A SYSTEM MODEL FOR SHORT AND LONG TERM PREDICTION OF GRAZING PASTURE PRODUCTIVITY

1983 ◽  
Vol 4 (0) ◽  
pp. 53-65
Author(s):  
Masae SHIYOMI ◽  
Shigeo TAKAHASHI ◽  
Tsuyoshi AKIYAMA ◽  
Tomoyuki HAKAMADA
Keyword(s):  
System Model ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction ◽  
Pasture Productivity
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