An Enhanced LSTM for Trend Following of Time Series

Hu, Yao; Sun, Xiaoyan; Nie, Xiaodong; Li, Yuzhu; Liu, Lian

doi:10.1109/access.2019.2896621

Cited by 66 publications

(25 citation statements)

References 34 publications

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“…Hu et al [36] proposed a LSTM network that aggregates the PSO algorithm for safety forecast model. Enhanced PSO-GD aggregated LSTM is best suited for the analysis for Time-series data.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

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“…Hu et al [36] proposed a LSTM network that aggregates the PSO algorithm for safety forecast model. Enhanced PSO-GD aggregated LSTM is best suited for the analysis for Time-series data.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient sentiment analysis methodology based on long short-term memory networks

Shobana

Murali

2021

Complex Intell. Syst.

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“…The LSTM is evolved from the recurrent neural network [44], and it is suitable for processing and predicting events with relatively long intervals and delays in time series [45]. One of its advantages is that it can avoid the gradient vanishing problem in traditional recurrent neural networks.…”

Section: Lstm-based Flow Predictionmentioning

confidence: 99%

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Gui

Zhou

Wang

et al. 2020

IEEE Trans. Veh. Technol.

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Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this paper, we first form an aviation big data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long shortterm memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.

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“…PSO with gradient descent (GD) were used to perform the trend following of electromagnetic radiation intensity data sampled from a coal mine and the atmospheric particulate PM2.5 matter data from the US Embassy in Beijing for safety forecast [16]. The improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA).…”

Section: Lstm Forecasting With Pso Approachmentioning

confidence: 99%

Timeseries Forecasting using Long Short-Term Memory Optimized by Multi Heuristics Algorithm

Hendri,

Wibowo*,

Sari

2019

IJRTE

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Forecasting future price of financial instruments (such as equity, bonds and mutual funds) has become an ongoing effort of financial and capital market industry members. The most current technology is usually applied by high economic scale companies to solve the ambitious and complicated problem. This paper presents optimization solution for a deep learning model in forecasting selected Indonesian mutual funds' Net Asset Value (NAV). There is a well-known issue in determining a deep learning parameters in LSTM network like window timestep and number of neurons to be used in getting the optimal learning from the historical data. This research tries to provide solution by utilizing multi-heuristics optimization approach consists of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to determine the best LSTM's network parameters, namely window timesteps and number of neurons. The result shows that from the nine selected mutual funds, PSO outperforms GA in optimizing the LSTM model by giving a lower Root Square Mean Error (RMSE) by 460.84% compared to GA's. However, PSO took a longer execution time by 1.78 times of GA's. This paper also confirms that based on RMSE for both training and evaluation dataset, equity mutual fund's forecasted NAV has the highest RMSE followed by fixed income mutual fund's forecasted NAV and money market mutual fund forecasted NAV.

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An Enhanced LSTM for Trend Following of Time Series

Cited by 66 publications

References 34 publications

An efficient sentiment analysis methodology based on long short-term memory networks

An efficient sentiment analysis methodology based on long short-term memory networks

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Timeseries Forecasting using Long Short-Term Memory Optimized by Multi Heuristics Algorithm

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