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The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their fu...
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The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock t...
Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integr...
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Citations
... In 2021, Wu and Ming-Tai proposed the SACLSTM stock price prediction algorithm, which constructs a sequence array of historical data and its leading indicators and uses the array as the input image of the CNN framework, and this algorithm 2 Complexity has achieved excellent forecasting results for Taiwan and American stocks [22], which is similar to the work proposed by the authors in reference [23]. An LSTM-GA stock trading suggestion system in IOT was proposed, based on historical data and leading indicators [24]. In 2022, Zhang et al. ...
Stock price prediction is an important and complex time-series problem in academia and financial industries. Stock market prices are voted by all kinds of investors and are influenced by various factors. According to the literature studies, such as Elliott’s wave theory and Howard’s market cycle investment theory, the cyclic patterns are significant characteristics of the stock market. However, even several studies that do consider cyclic patterns (or similar concepts) suffered from the data leakage or boundary problems, which could be impractical for real applications. Inspired by the abovementioned, we propose a hybrid deep learning model called mWDN-LSTM, which correctly utilizes the cyclic patterns’ information to predict stock price while avoiding the data leakage and alleviating boundary problems. According to the experiments on two different datasets, our model mWDN-LSTM outperforms the well-known benchmarks such as CNN-LSTM on the same experimental setup and demonstrates the effectiveness of utilizing cyclic patterns in stock price prediction.
... One deep learning technique that can be used for time series is the Recurrent Neural Network (RNN), which is designed to work with sequential data [10] [11]. The progress of RNN is growing quite rapidly in various fields, but RNN has a weakness in processing time series because, [12] performance for prediction will have a negative effect if the sequence size is relevantly long and the other is that the RNN gradient will be lost, resulting in longterm memory failure. Unlike the RNN, LSTM can manage memory for each input by using memory cells and gate units [13]. ...
Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is represented by forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.
... One of the deep learning that can work for time series is the Recurrent Neural Network (RNN), which is designed to work with sequential data [10] [11]. The progress of RNN is growing quite rapidly in various fields, but RNN has a weakness in processing time series because, [12] performance for prediction will have a negative effect if the sequence size is relevantly long and the other is that the RNN gradient will be lost, resulting in long-term memory failure. Hochreiter et al. created LSTM as a floating RNN to deal with these weaknesses [13]. ...
Fluctuating stock prices make it difficult for investors to see investment opportunities. One tool that can help investors overcome this is forecasting techniques. Long Short-Term Memory (LSTM) is one of deep learning methods used in forecasting time series. The training and success of deep learning is strongly influenced by the selection of hyperparameters. This research uses a hybrid method between the Genetic Algorithm (GA) and LSTM to find a suitable model for predicting stock prices. GA is used in optimizing the architecture such as the number of epochs, window size, and the number of LSTM units in the hidden layer. Tuning optimizer is also carried out using several optimizers to achieve the best value. From method that has been applied, it shows that the method has a good level of accuracy with MAPE values below 10% in every optimizer used. The error rate generated is quite low, in case-1 with a minimum RMSE value of 93.03 and 94.40, & in case-2 with an RMSE value of 104.99 and 150.06 during training and testing. A fairly stable and small value is generated by setting it using the Adam Optimizer.
... MISA was tested using the HKEx 2001 stock market database, and historical market prices and news. A novel hybrid strategy based on machine learning and filtering approaches was presented by Wu et al. (2021b). The major goal of their research was to show enhanced stock market forecasting and compare the suggested model's performance to that of other best models. ...
Stock values are predicted using the market's biggest issues. Stock price data is hard to predict due to its distinct traits and volatility. Online news and comments reflect investor sentiment and thoughts on stocks, which may assist predict stock prices. Most authors lately recommended a statistical and environmental measurement-based technique for anticipating machine learning or stock price changes. These models struggle with changing data. We propose a hybrid machine learning technique for long-term stock market price trend prediction (LT-SMF). We employed improved butterfly optimization (IBO) to remove artefacts from input data. Scaling, polarising, and variation percentage are used to find valuable qualities. Second, a brown Planthopper optimization (BPO) approach reduces data dimensionality for optimal feature selection. To forecast stock market price variations, a hybrid FEL-DNN was utilized. Using 11 stock market indices and social media data, evaluate the LT-SMF model. Simulation performance was compared to state-of-the-art models for mean square error, mean bios error, mean absolute error, root mean square error, accuracy, precision, recall, and F-measure. The proposed FEL-DNN classifier outperforms the current state-of-the-art CNN3D, CNN3D − DR, LSTM − D, CNN3D + LSTM, CNN3D − D + LSTM, and CNN3D − DR + LSTM classifiers by 38.67%, 41.71%, 39.32%, 36.04%, 41.13%, and 43.43% respectively in terms of accuracy in the social media data.
... In solving complex optimization problems, intelligent optimization algorithms have incomparable advantages over traditional optimization methods. At present, intelligent optimization algorithms are widely used to solve optimization problems in various fields, such as engineering optimization [10][11], intelligent scheduling [12][13], image processing [14][15], wireless sensor networks [16][17][18], path optimization [19][20], data prediction [21][22] and so on [23][24]. ...
With the increasing complexity of optimization problems, the requirements for algorithm optimization capabilities are getting higher and higher. In order to better solve complex optimization problems, this paper proposes a new swarm intelligence optimization algorithm named Tumbleweed Optimization Algorithm (TOA). The TOA algorithm consists of two stages, which simulate the seedling growth phase and seed propagation phase of tumbleweed respectively. The TOA algorithm adopts a multi-group structure to improve the global searching ability of the algorithm. In order to verify the performance of the TOA algorithm in numerical optimization and solving practical application problems, this paper selects the CEC2013 benchmark function library and the vehicle path planning in the smart city for testing. Through the comparison of experimental results, the TOA algorithm can both show strong optimization capabilities. Compared with the other ten intelligent optimization algorithms, the TOA algorithm proposed in this paper can also show strong competitiveness.
... It is reported that Elman with direct input-output connection helps in improving accuracy, while reducing network complexity and computational cost. Recent work by researchers (Wu et al. 2021b) proposed a trading suggestion system based on Synergetic LSTM-GA. Their experimental outcomes over five Taiwan stocks revealed that LSTMLI-GA framework was able to achieve a higher profit margin as compared to other models. ...
It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM's performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4–6%, 25–31%, and accuracy improvements of 0.4–0.8%, 0.9–1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.
... Introduction. In real life, there are many engineering problems to be solved, such as deep learning, data mining, intelligent computing, etc [1,2]. As a powerful tool, intelligent computing can solve many problems that could not be solved in real life. ...
The meta-heuristic algorithm is used in the research of various complex problems. Due to the limitations of the original meta-heuristic algorithm, many improved meta-heuristic algorithms have been proposed, such as compact, adaptive, multi-objective and parallel schemes. Among them, the parallel strategy may get a significant improvement for related applications. This paper mainly studies the application of parallel computing in meta-heuristic algorithms. There are two main types of parallelism: one is absolute parallelism, using multiple processors which can solve optimization problems with high computational costs and improve execution efficiency. The other is virtual parallelism (multi-grouping), which decomposes the population into multiple sub-populations, and each sub-population communicates between species to generate better solutions. In addition, the combination of parallel computing and meta-heuristic algorithms can solve a wide variety of application problems: path planning, engineering design, large-scale optimization, image segmentation, neural networks and prediction problems, etc. This paper presents a comprehensive study and systematic survey of parallel meta-heuristics.
... Quantitative trading can be realized automatically through technologies such as computers. Different from general trading methods, the behavior of quantitative trading is to use computer technology to mine relevant information from the historical data of the stock market to increase the profit of the transaction [3,4]. Therefore, to develop a quantitative trading strategy that can increase returns is what researchers hope. ...
The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.
Particulate matter forecasting is fundamental for early warning and controlling air pollution, especially PM
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. The increase in this level of concentration will lead to a negative impact on public health. This study develops a hybrid model of CNN-LSTM and CONV-LSTM by combining a convolutional neural network (CNN) with an LSTM network to forecast PM
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concentration for the next few hours in Kemayoran DKI Jakarta, which is known as a busy area. We discovered the advantages of CNN in effectively extracting features and LSTM in learning long-term historical data from PM
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concentration time series data. The predictive model of CNN-LSTM is carried out in a different architecture where the CNN process is carried out first to become the input of LSTM. For CONV-LSTM, it is carried out in one architecture where the multiplication in the LSTM architecture is coupled with the convolution process. This research will explain how the method of developing hybrid CNN-LSTM and CONV-LSTM in predicting PM
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concentrations. Based on metric evaluation, the two models are compared to find the best model. Both predictive models produce MAPE values that fall into the good enough category with values <20%. Results were obtained for CONV-LSTM with MAE worth 6.52, RMSE 8.55, and MAPE 16.39%. As a result, the CONV-LSTM model performs better than CNN-LSTM in nowcasting PM
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.
