LSTM-DDPG for Trading with Variable Positions

Jia, Zhichao; Gao, Qiang; Peng, Xiaohong

doi:10.3390/s21196571

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…SARIMA and ANN models have been successfully used to fit and predict time series data in a variety of fields [ 18 – 22 ]. The SARIMA model can fit seasonal fluctuations well, but the fitting accuracy is poor for nonlinear components of TS data [ 23 ], while the LSTM model can compensate for this deficiency well, but another problem is that the mandatory fitting of seasonal fluctuations using a single LSTM model over a longer period increases the risk of overfitting, so a hybrid SARIMA-LSTM model was used to solve the accuracy problem of nonlinear fitting and simulate seasonal fluctuations at the same time [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Wang,

Zhang

et al. 2024

BMC Infect Dis

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Background Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. Methods Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models. Results The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. Conclusions The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

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Section: Discussionmentioning

confidence: 99%

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Wang,

Zhang

et al. 2024

BMC Infect Dis

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“…Recurrent neural network (RNN) is mainly used for solving the shortterm memory issue in a basic neural network. Few researches [48], [73], [74] pioneered the integrated the RNN with DRL to learn sequential data. In RS domain, [63], [75] employed the hybrid RNN in RL algorithm and demonstrated the ability of capturing long-term sequential information.…”

Section: B Recdqnmorsmentioning

confidence: 99%

Multiobjective Deep Reinforcement Learning for Recommendation Systems

et al. 2022

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Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty and diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering and combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start problem from collaborative filtering, it also vulnerable to highly sparse environment, while the evolutionary algorithm suffers from premature convergence and curse of dimensionality. These limitations have prompted this work to propose deep reinforcement learning (DRL) approaches for MO optimization in RSs. Several works in DRL are available but none has addressed MO RS problems. In this study, the performances of proposed DRL approaches that based on Deep Q-Network in MO recommendation problem were investigated. The approaches were evaluated with movie recommendation dataset by using three conflicting metrics, namely precision, novelty, and diversity. The results demonstrated that deep reinforcement learning approaches has superiority performance in MO optimization, and its capability of recommending precise item along with achieving high novelty and diversity against the benchmark that using probabilistic based multiobjective approach based on evolutionary algorithm (PMOEA). Although PMOEA algorithm secured higher average value in precision, it has lower values of novelty and diversity than the proposed DRL approaches. The DRL approaches surpassed the benchmark results in average of maximum novelty and the average of mean diversity metrics, the optimization between accuracy and non-accuracy metrics is inevitable. In addition, the experiments revealed that incorporation of user latent features enhanced the recommendation quality.

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“…Li et al [9] proposed an RL scheme for short-term stock price movement prediction based on the actor-critic and critics-only RL methods respectively. Jia et al [10] used LSTM neural network to extract market state characteristics and DDPG framework to judge trading decisions, and thus proposed a deep reinforcement learning model named LSTM-DDPG to make trading decisions. Li et al [11] proposed PPO enhancement strategy to modify the signal of stock trading strategy rather than directly predict the direction of stock price, and the experiment proved that the proposed PPO enhancement strategy was superior to the benchmark test.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Supervised Reinforcement Learning for Stock Trading

2023

IEEE Access

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Deep reinforcement learning (DRL) algorithm is often used to find the best trading strategy in algorithmic trading. However, the classical DRL model is difficult to achieve rapid convergence, and the features extracted from the market data are relatively simple, resulting in incomplete DRL learning information. In this paper, we propose a supervised reinforcement learning method, a hybrid optimal investment strategy formation method consisting of long short-term memory neural network (LSTM) and deep deterministic policy gradient (DDPG). By participating in reinforcement learning in the early stage of supervised learning, agents can obtain guiding prior experience, thus reducing the cost of agent learning and accelerating convergence. In addition, multi-feature state input is added to the model to optimize the agent's learning of the environment. Compared with DDPG algorithm, LSTM-DDPG algorithm achieves convergence faster. Experiments on three regional stock markets in China, the United States and Europe show that LSTM-DDPG algorithm has higher profit and lower risk than B&H, MACD and LSTM trading strategies.INDEX TERMS Supervised reinforcement learning, finance and operations, reinforcement learning, deep deterministic policy gradient, long short-term memory.

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LSTM-DDPG for Trading with Variable Positions

Cited by 7 publications

References 24 publications

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

Multiobjective Deep Reinforcement Learning for Recommendation Systems

Multi-Feature Supervised Reinforcement Learning for Stock Trading

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