Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon

Preeti, .; Bala, Rajni; Singh, Ram

doi:10.1109/icccnt45670.2019.8944624

Cited by 25 publications

(8 citation statements)

References 19 publications

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“…There are many attempts to corporate LSTM with investment. Preeti, R. Bala and R. P. Singh's work in 2019 confirms that LSTM is effective for time series prediction, even in some instances where the data is non-stationary [8]. Vora, Shaikh and Bhanushali's research using LSTM models with features including open, closed, lowest, highest, date, and everyday transaction size of stock data to forecast stock prices in India market suggests that although LSTM model has few restraints including a forecast time lag, but can still use the attention level to foretell stock prices [9].…”

Section: Lstmmentioning

confidence: 87%

Application of LSTM and portfolio optimization in Chinese stock market

Zheng¹

2022

BCPBM

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The purpose of this paper is to examine the application of LSTM and mean variance portfolio optimization in Chinese stock market. 20 stocks are selected from CSI 300 components, we collect their High, Low, Open, Adjust Close and trade volume from June 16th 2020 to June 16th 2022. Then we use LSTM model to forecast the stock price. The forecast results are used to construct 2 portfolios. One portfolio maximize Sharpe ratio, the other portfolio minimize variance. From April 6th to June 16th 2022, the Maximize Sharpe Ratio portfolio outperformed CSI 300 index, the Minimize Variance Portfolio did not beat the market but the return was very close to CSI. Therefore, the combination of LSTM and Mean Variance Portfolio Optimization theory is effective in Chinese stock market.

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Section: Lstmmentioning

confidence: 87%

Application of LSTM and portfolio optimization in Chinese stock market

Zheng¹

2022

BCPBM

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“…For example, ARIMA models focus on linear relationships in the time series, while LSTM networks capture non-linearity, or using neural networks reduces error rates. Furthermore, as shown by Siami-Namini et al (2019), the performance of an LSTM network is much more accurate; moreover, this architecture allows to overcome the non-stationarity of prices (Preeti et al 2019).…”

Section: Neural Network and Lstm Unitsmentioning

confidence: 99%

Forecasting financial time series with Boltzmann entropy through neural networks

Grilli

Santoro

2022

Comput Manag Sci

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Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex.

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“…The proposed prediction model, attn-LSTM, will be trained on the training set with the set of optimal hyper-parameters from hyper-tuning, and the results are reported by predicting the unseen testing set. and is capable of learning long-term time series data as well as short-term time series data (61). The hidden layer inside an LSTM network contains recurrently connected special units called memory cells and their corresponding gate units: input gate, forget gate, and output gate (60) as shown in Figure 4.…”

Section: Proposed Model Architecturementioning

confidence: 99%

Profiling hearing aid users through big data explainable artificial intelligence techniques

Iliadou

Kikidis

et al. 2022

Front. Neurol.

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Debilitating hearing loss (HL) affects ~6% of the human population. Only 20% of the people in need of a hearing assistive device will eventually seek and acquire one. The number of people that are satisfied with their Hearing Aids (HAids) and continue using them in the long term is even lower. Understanding the personal, behavioral, environmental, or other factors that correlate with the optimal HAid fitting and with users' experience of HAids is a significant step in improving patient satisfaction and quality of life, while reducing societal and financial burden. In SMART BEAR we are addressing this need by making use of the capacity of modern HAids to provide dynamic logging of their operation and by combining this information with a big amount of information about the medical, environmental, and social context of each HAid user. We are studying hearing rehabilitation through a 12-month continuous monitoring of HL patients, collecting data, such as participants' demographics, audiometric and medical data, their cognitive and mental status, their habits, and preferences, through a set of medical devices and wearables, as well as through face-to-face and remote clinical assessments and fitting/fine-tuning sessions. Descriptive, AI-based analysis and assessment of the relationships between heterogeneous data and HL-related parameters will help clinical researchers to better understand the overall health profiles of HL patients, and to identify patterns or relations that may be proven essential for future clinical trials. In addition, the future state and behavioral (e.g., HAids Satisfiability and HAids usage) of the patients will be predicted with time-dependent machine learning models to assist the clinical researchers to decide on the nature of the interventions. Explainable Artificial Intelligence (XAI) techniques will be leveraged to better understand the factors that play a significant role in the success of a hearing rehabilitation program, constructing patient profiles. This paper is a conceptual one aiming to describe the upcoming data collection process and proposed framework for providing a comprehensive profile for patients with HL in the context of EU-funded SMART BEAR project. Such patient profiles can be invaluable in HL treatment as they can help to identify the characteristics making patients more prone to drop out and stop using their HAids, using their HAids sufficiently long during the day, and being more satisfied by their HAids experience. They can also help decrease the number of needed remote sessions with their Audiologist for counseling, and/or HAids fine tuning, or the number of manual changes of HAids program (as indication of poor sound quality and bad adaptation of HAids configuration to patients' real needs and daily challenges), leading to reduced healthcare cost.

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Financial and Non-Stationary Time Series Forecasting using LSTM Recurrent Neural Network for Short and Long Horizon

Cited by 25 publications

References 19 publications

Application of LSTM and portfolio optimization in Chinese stock market

Application of LSTM and portfolio optimization in Chinese stock market

Forecasting financial time series with Boltzmann entropy through neural networks

Profiling hearing aid users through big data explainable artificial intelligence techniques

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