A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

Liang, Sisheng; Nguyen, Long

doi:10.1109/bigdata.2018.8622332

Cited by 47 publications

(29 citation statements)

References 9 publications

(13 reference statements)

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“…e best model among a set of ML neural network-based models, involving MLP, DRNN, and stacked LSTM, was determined. According to [46], real-time dynamics constitute the most challenging aspect of wind speed forecasting. We determined the stacked LSTM model to be the best for wind speed forecasting due to its appropriate handling of long-and short-term time dependency.…”

Section: Methodology and Algorithmsmentioning

confidence: 99%

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Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Wei

2020

Advances in Meteorology

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Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.

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Section: Methodology and Algorithmsmentioning

confidence: 99%

“…An LSTM network is identical to a standard RNN, with the exception of the summation units in the hidden layer being replaced by memory blocks [56]. Equations (2)-(6) describe how output values are updated at each step [22,46,57]. Specifically,…”

Section: Framework Underlying the Proposed Neural Networkmentioning

confidence: 99%

Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Wei

2020

Advances in Meteorology

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“…To capture the real-time dynamics of input, Addict Free utilizes a LSTM model for relapse time series prediction due to its wellhandling ability of long and short term time dependency. The model is also utilized in other types of prediction as in [4] and [1][2][3]. Figure 5 shows the basic structure of LSTM.…”

Section: Methods 41 Relapse Predictionmentioning

confidence: 99%

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Yang

Reddy

Kesidi

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called "Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users. CCS CONCEPTS• Applied computing → Health care information systems.

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“…Vector autoregressive (VAR) [8], a statistical multivariate model, and machine learning (ML) approaches, such as support vector regression (SVR) [9] and random forest regression [10], can achieve higher accuracy than classical predictive models; yet, they fail to fully capture spatial relations. More recently, some progress has been made by applying neural networks 1 (NNs) to predict ST data [1], [2], [11]- [14]. NNs have the capacity of not only mapping an input data to an output, but also of learning a useful representation to improve the mapping accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

“…[32]- [37] other [38], [39] No RNN [17], [20], [40]- [44], other [2] , [19], [45]- [49] wind Yes RNN [50] No RNN [14] other [51]- [53] meteorological Yes AE [54] No RNN [16] other [5], [55] body-motion Yes RNN [56]- [58] neuroscience Yes Conv. [59] No RBM [60] semantic Yes RNN [61] cortex may be unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning

Lewenfus

Martins

Chatzinotas

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series forecasting (temporal prediction) and graphsignal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed learning model outperforms state-of-the-art deep learning techniques, especially when predictions are made using a small subset of network nodes, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications. Index Terms-Multivariate time series, forecasting and interpolation, deep learning, recurrent neural networks (RNNs), graph signal processing (GSP) Björn Ottersten (S'87-M'89-SM'99-F'04) was born in Stockholm, Sweden, in 1961. He received the M.S. degree in electrical engineering and ap

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A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

Cited by 47 publications

References 9 publications

Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Addict Free - A Smart and Connected Relapse Intervention Mobile App

Joint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning

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