Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow
Sign Up to like & getrecommendations! Published in 2018 at "Water Resources Management"
DOI: 10.1007/s11269-018-2094-2
Abstract: Accurate reservoir-inflow forecasting is especially important for optimizing operation of multi-propose reservoirs that provide hydropower generation, flood control, and water for domestic use and irrigation. There are no previous reports of successful daily flow prediction… read more here.
Keywords: rmse; lead time; time; model ... See more keywords
Estimation of Auto-Regressive models for time series using Binary or Quantized Data
Sign Up to like & getrecommendations! Published in 2018 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2018.09.221
Abstract: Abstract In this paper, we first present an algorithm for the estimation of an Auto-Regressive model of time series using output data of a binary sensor. This algorithm is based on the estimation of the… read more here.
Keywords: auto regressive; estimation auto; time series; time ... See more keywords
Probabilistic Wind Power Forecasting Using Optimized Deep Auto-Regressive Recurrent Neural Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3160696
Abstract: Wind power forecasting is very crucial for power system planning and scheduling. Deep neural networks (DNNs) are widely used in forecasting applications due to their exceptional performance. However, the DNNs’ architectural configuration has a significant… read more here.
Keywords: auto regressive; wind power; power; power forecasting ... See more keywords
Probabilistic Real-Time Thermal Rating Forecasting for Overhead Lines by Conditionally Heteroscedastic Auto-Regressive Models
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Power Delivery"
DOI: 10.1109/tpwrd.2016.2577140
Abstract: Conventional approaches to forecasting of real-time thermal ratings (RTTRs) provide only single-point estimates with no indication of the size or distribution of possible errors. This paper describes weather-based methods to estimate probabilistic RTTR forecasts for… read more here.
Keywords: overhead lines; real time; time thermal; auto regressive ... See more keywords
Testing for high-dimensional network parameters in auto-regressive models
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DOI: 10.1214/19-ejs1646
Abstract: High-dimensional auto-regressive models provide a natural way to model influence between $M$ actors given multi-variate time series data for $T$ time intervals. While there has been considerable work on network estimation, there is limited work… read more here.
Keywords: high dimensional; network; time; regressive models ... See more keywords
Tracking an Auto-Regressive Process with Limited Communication per Unit Time †
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23030347
Abstract: Samples from a high-dimensional first-order auto-regressive process generated by an independently and identically distributed random innovation sequence are observed by a sender which can communicate only finitely many bits per unit time to a receiver.… read more here.
Keywords: communication; time; regressive process; per unit ... See more keywords