Academic literature on the topic 'RNN LSTM'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'RNN LSTM.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "RNN LSTM"
1
Noh, Seol-Hyun. "Analysis of Gradient Vanishing of RNNs and Performance Comparison." Information 12, no. 11 (2021): 442. http://dx.doi.org/10.3390/info12110442.
Full textAbstract:
A recurrent neural network (RNN) combines variable-length input data with a hidden state that depends on previous time steps to generate output data. RNNs have been widely used in time-series data analysis, and various RNN algorithms have been proposed, such as the standard RNN, long short-term memory (LSTM), and gated recurrent units (GRUs). In particular, it has been experimentally proven that LSTM and GRU have higher validation accuracy and prediction accuracy than the standard RNN. The learning ability is a measure of the effectiveness of gradient of error information that would be backpropagated. This study provided a theoretical and experimental basis for the result that LSTM and GRU have more efficient gradient descent than the standard RNN by analyzing and experimenting the gradient vanishing of the standard RNN, LSTM, and GRU. As a result, LSTM and GRU are robust to the degradation of gradient descent even when LSTM and GRU learn long-range input data, which means that the learning ability of LSTM and GRU is greater than standard RNN when learning long-range input data. Therefore, LSTM and GRU have higher validation accuracy and prediction accuracy than the standard RNN. In addition, it was verified whether the experimental results of river-level prediction models, solar power generation prediction models, and speech signal models using the standard RNN, LSTM, and GRUs are consistent with the analysis results of gradient vanishing.
2
Alam, Muhammad S., AKM B. Hossain, and Farhan B. Mohamed. "Performance Evaluation of Recurrent Neural Networks Applied to Indoor Camera Localization." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 116–24. http://dx.doi.org/10.46338/ijetae0822_15.
Full textAbstract:
Researchers in robotics and computer vision are experimenting with the image-based localization of indoor cameras. Implementation of indoor camera localization problems using a Convolutional neural network (CNN) or Recurrent neural network (RNN) is more challenging from a large image dataset because of the internal structure of CNN or RNN. We can choose a preferable CNN or RNN variant based on the problem type and size of the dataset. CNN is the most flexible method for implementing indoor localization problems. Despite CNN's suitability for hyper-parameter selection, it requires a lot of training images to achieve high accuracy. In addition, overfitting leads to a decrease in accuracy. Introduce RNN, which accurately keeps input images in internal memory to solve these problems. Longshort-term memory (LSTM), Bi-directional LSTM (BiLSTM), and Gated recurrent unit (GRU) are three variants of RNN. We may choose the most appropriate RNN variation based on the problem type and dataset. In this study, we can recommend which variant is effective for training more speedily and which variant produces more accurate results. Vanishing gradient issues also affect RNNs, making it difficult to learn more data. Overcome the gradient vanishing problem by utilizing LSTM. The BiLSTM is an advanced version of the LSTM and is capable of higher performance than the LSTM. A more advanced RNN variant is GRU which is computationally more efficient than an LSTM. In this study, we explore a variety of recurring units for localizing indoor cameras. Our focus is on more powerful recurrent units like LSTM, BiLSTM, and GRU. Using the Microsoft 7-Scenes and InteriorNet datasets, we evaluate the performance of LSTM, BiLSTM, and GRU. Our experiment has shown that the BiLSTM is more efficient in accuracy than the LSTM and GRU. We also observed that the GRU is faster than LSTM and BiLSTM
3
Muhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks." Information 11, no. 5 (2020): 243. http://dx.doi.org/10.3390/info11050243.
Full textAbstract:
An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.
4
Wang, Yiyang, Wenchuan Wang, Hongfei Zang, and Dongmei Xu. "Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin." Water 15, no. 22 (2023): 3928. http://dx.doi.org/10.3390/w15223928.
Full textAbstract:
The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.
5
Zaheer, Shahzad, Nadeem Anjum, Saddam Hussain, et al. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model." Mathematics 11, no. 3 (2023): 590. http://dx.doi.org/10.3390/math11030590.
Full textAbstract:
Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.
6
Liu, Di, Qingyuan Xia, Changhui Jiang, Chaochen Wang, and Yuming Bo. "A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage Bridging." International Journal of Aerospace Engineering 2020 (August 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/2975489.
Full textAbstract:
Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.
7
Lee, Ju-Hyung, and Jun-Ki Hong. "Comparative Performance Analysis of RNN Techniques for Predicting Concatenated Normal and Abnormal Vibrations." Electronics 12, no. 23 (2023): 4778. http://dx.doi.org/10.3390/electronics12234778.
Full textAbstract:
We analyze the comparative performance of predicting the transition from normal to abnormal vibration states, simulating the motor’s condition before a drone crash, by proposing a concatenated vibration prediction model (CVPM) based on recurrent neural network (RNN) techniques. Subsequently, using the proposed CVPM, the prediction performances of six RNN techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gate recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional-GRU (Bi-GRU), are analyzed comparatively. In order to assess the prediction accuracy of these RNN techniques in predicting concatenated vibrations, both normal and abnormal vibration data are collected from the motors connected to the drone’s propellers. Consequently, a concatenated vibration dataset is generated by combining 50% of normal vibration data with 50% of abnormal vibration data. This dataset is then used to compare and analyze vibration prediction performance and simulation runtime across the six RNN techniques. The goal of this analysis is to comparatively analyze the performances of the six RNN techniques for vibration prediction. According to the simulation results, it is observed that Attn.-LSTM and Attn.-GRU, incorporating the attention mechanism technique to focus on information highly relevant to the prediction target through unidirectional learning, demonstrate the most promising predictive performance among the six RNN techniques. This implies that employing the attention mechanism enhances the concentration of relevant information, resulting in superior predictive accuracy compared to the other RNN techniques.
8
Musyoka, Gabriel, Antony Waititu, and Herbert Imboga. "Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions." International Journal of Statistical Distributions and Applications 10, no. 2 (2024): 16–24. http://dx.doi.org/10.11648/j.ijsd.20241002.11.
Full textAbstract:
In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) have all been used, but their results have proven less than satisfactory. The project focuses on developing a credit scoring model specifically tailored for digital financial institutions, by leveraging a hybrid model that combines long short-term memory (LSTM) networks with recurrent neural networks (RNN). This innovative approach capitalizes on the strengths of the Long-Short Term Memory (LSTM) for long-term predictions and Recurrent Neural Network (RNN) for its recurrent neural network capabilities. A key component of the approach is feature selection, which entails extracting a subset of pertinent features from the credit risk data using RNN in order to help classify loan applications. The researcher chose to use data from Kaggle to study and compare the efficacy of different models. The findings reveal that the RNN-LSTM hybrid model outperforms other RNNs, LSTMs, and traditional models. Specifically, the hybrid model demonstrated distinct advantages, showcasing higher accuracy and a superior Area Under the Curve (AUC) compared to individual RNN and LSTM models. While RNN and LSTM models exhibited slightly lower accuracy individually, their combination in the hybrid model proved to be the optimal choice. In summary, the RNN-LSTM hybrid model developed stands out as the most effective solution for predicting credit risk in digital financial institutions, surpassing the performance of standalone RNN and LSTM models as well as traditional methodologies. This research contributes valuable insights for banks, regulators, and investors seeking robust credit risk assessment tools in the dynamic landscape of digital finance.
9
Ahmad, M. Haroon, Ali Saeed, M. Usman Bhatti, Naveed Hussain, Muhammad Farhat Ullah, and Mehmood Anwar. "Next Word Prediction for Urdu using Deep Learning Techniques." VFAST Transactions on Software Engineering 13, no. 1 (2025): 49–59. https://doi.org/10.21015/vtse.v13i1.2044.
Full textAbstract:
A language model for next-word prediction is a probabilistic representation of a natural language that utilizes text corpora to generate word probabilities. These models play a crucial role in text generation, machine translation, and question-answering applications. The focus of this study is to develop an improved algorithm for next-word prediction in Urdu. The study implements deep learning models, including RNN, LSTM, and Bi-LSTM, on a subset of the Ur-Mono Urdu corpus containing 3,000 and 5,000 sentences. To prepare the data for experimentation, tokenization and stemming data cleaning techniques are applied. The study achieved an accuracy of 87% using the RNN model on the first 3,000 sentences of the Ur-Mono dataset and 84% accuracy using the RNN model on the first 5,000 sentences of the Ur-Mono dataset. In conclusion, it can be stated that when the corpus size is small, the RNN outperforms both the LSTM and BiLSTM. However, as the corpus size increases, the Bi-LSTM exhibits superior performance compared to both RNN and LSTM.
10
Ali, Murtadha. "Improving Network Security: An Intelligent IDS with RNN-LSTM and Grey Wolf Optimization." Wasit Journal of Computer and Mathematics Science 3, no. 4 (2024): 1–14. https://doi.org/10.31185/wjcms.264.
Full textAbstract:
While this dependence on interconnected computer networks and the web requires robust cybersecurity. Cyber threats have been met with solutions like Intrusion Detection Systems (IDS). IDS are commonly rule-based, and very often use either signature-based or heuristic approaches to detect intrusions. Therefore, for such we recommend an IDS that merges the Grey Wolf Optimization (GWO) algorithm and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM). RNN-LSTM to Handle Dynamic Network data, but not provided enough complain details in model training. Based on the behavior of grey wolf, an optimization technique GWO is implemented for intrusion detection to enhance accuracy and minimize false alarm in RNN-LSTM. Preprocess and segment network data with creating RNN-LSTM model for considering the dependence of our dataset Our approach improves the IDS performance by optimizing hyperparameters such as hidden layers, units, learning rates using GWO. The architecture of this RNN-LSTM with GWO IDS provides capable and responsive intrusion detection, training on previous data to be able to detect new threats. Made for network security by combining deep learning and optimization, tests reached 99.5% accurate. The research advances IDSs, addressing the limitations of traditional systems, and underscores the potential of AI and optimization in complex network security. This study demonstrates the promise of RNN-LSTM and GWO for creating robust, adaptive intrusion detection systems in intricate network environments.
Dissertations / Theses on the topic "RNN LSTM"
1
Singh, Akash. "Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215723.
Full textAbstract:
We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks with fixed size time windows over inputs. Our experiments, with three real-world datasets, show that while LSTM RNNs are suitable for general purpose time series modeling and anomaly detection, maintaining LSTM state is crucial for getting desired results. Moreover, LSTMs may not be required at all for simple time series.<br>Vi undersöker Long short-term memory (LSTM) för avvikelsedetektion i tidsseriedata. På grund av svårigheterna i att hitta data med etiketter så har ett oövervakat an-greppssätt använts. Vi tränar rekursiva neuronnät (RNN) med LSTM-noder för att lära modellen det normala tidsseriemönstret och prediktera framtida värden. Vi undersö-ker olika sätt av att behålla LSTM-tillståndet och effekter av att använda ett konstant antal tidssteg på LSTM-prediktionen och avvikelsedetektionsprestandan. LSTM är också jämförda med vanliga neuronnät med fasta tidsfönster över indata. Våra experiment med tre verkliga datasetvisar att även om LSTM RNN är tillämpbara för generell tidsseriemodellering och avvikelsedetektion så är det avgörande att behålla LSTM-tillståndet för att få de önskaderesultaten. Dessutom är det inte nödvändigt att använda LSTM för enkla tidsserier.
2
Cavallie, Mester Jon William. "Using LSTM Neural Networks To Predict Daily Stock Returns." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106124.
Full textAbstract:
Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. We used daily stock trading data to let an LSTM train models at predicting daily returns for 60 stocks from the OMX30 and Nasdaq-100 indices. Subsequently, we measured their accuracy, precision, and recall. The mean accuracy was 49.75 percent, meaning that the observed accuracy was close to the accuracy one would observe by randomly selecting a prediction for each day and lower than the accuracy achieved by blindly predicting all days to be positive. Finally, we concluded that further improvements need to be made for models trained by LSTMs to have any notable predictive ability in the area of stock returns.
3
Yang, Yizhan. "Machine Learning Based Beam Tracking in mmWave Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292754.
Full textAbstract:
The demand for high data rates communication and scarcity of spectrum in existing microwave bands has been the key aspect in 5G. To fulfill these demands, the millimeter wave (mmWave) with large bandwidths has been proposed to enhance the efficiency and the stability of the 5G network. In mmWave communication, the concentration of the transmission signal from the antenna is conducted by beamforming and beam tracking. However, state-of-art methods in beam tracking lead to high resource consumption. To address this problem, we develop 2 machine-learning-based solutions for overhead reduction. In this paper, a scenario configuration simulator is proposed as the data collection approach. Several LSTM based time series prediction models are trained for experiments. Since the overhead is reduced by decreasing the number of sweeping beams in solutions, multiple data imputation methods are proposed to improve the performance of the solution. These methods are based on Multiple Imputation by Chained Equations (MICE) and generative adversarial networks. Both qualitative and quantitative experimental results on several types of datasets demonstrate the efficacy of our solution.<br>Efterfrågan på hög datahastighetskommunikation och brist på spektrum i befintliga mikrovågsband har varit nyckelaspekten i 5G. För att uppfylla dessa krav har millimetervåg (mmWave) med stora bandbredder föreslagits för att förbättra effektiviteten och stabiliteten i 5G-nätverket. I mmWavekommunikation utförs koncentrationen av överföringssignalen från antennen genom strålformning och strålspårning. Toppmoderna metoder inom strålspårning leder dock till hög resursförbrukning. För att lösa detta problem utvecklar vi två maskininlärningsbaserade lösningar för reduktion av omkostnader. I det här dokumentet föreslås en scenariokonfigurationssimulator som datainsamlingsmetod. Flera LSTM-baserade modeller för förutsägelse av tidsserier tränas för experiment. Eftersom omkostnaderna reduceras genom att minska svepstrålarna i lösningar föreslås flera datainputeringsmetoder för att förbättra lösningens prestanda. Dessa metoder är baserade på Multipel Imputation by Chained Equations (MICE) och generativa kontroversiella nätverk. Både kvalitativa och kvantitativa experimentella resultat på flera typer av datamängder visar effektiviteten i vår lösning.
4
Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.
Full textAbstract:
Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement.<br>Att förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.
5
Jablecka, Marta. "Modelling CLV in the Insurance Industry Using Deep Learning Methods." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273607.
Full textAbstract:
This paper presents a master’s thesis project in which deep learning methods are used to both calculate and subsequently attempt to maximize Customer Lifetime Value (CLV) for an insurance provider’s customers. Specifically, the report investigates whether panel data comprised of customers monthly insurance policy subscription history can be used with Recurrent Neural Networks (RNN) to achieve better predictive performance than the naïve forecasting model. In order to do this, the use of Long Short Term Memory (LSTM) for anomaly detection in a supervised manner is explored to determine which customers are more likely to change their subscription policies. Whether Deep Reinforcement Learning (DRL) can be used in this setting in order to maximize CLV is also investigated. The study found that the best RNN models outperformed the naïve model in terms of precision on the data set containing customers which are more likely to change their subscription policies. The models suffer, however, from several notable limitations so further research is advised. Selecting those customers was shown to be successful in terms of precision but not sensitivity which suggest that there is a room for improvement. The DRL models did not show a substantial improvement in terms of CLV maximization.<br>I detta examensarbete presenteras metoder där djupinlärning används för att både beräkna och maximera kundens lönsamhet över tid, Customer Lifetime Value (CLV), för en försäkringsleverantörs kunder. Specifikt undersöker rapporten historisk paneldata som består av kunders månatliga försäkringsinnehav där Recurrent Neural Networks (RNN) används för att uppnå bättre prediktiv prestanda än en naiv prognosmodell. Detta undersöks tillsammans med det neurala nätverket Long Short Term Memory (LSTM), där vi försöker finna anomalier på ett övervakat sätt. Där anomalier syftar på kunder som är mer benägna att ändra sin försäkringspolicy, då den största delen av populationen har samma innehav på månadsbasis. Även en gren av djupinlärning, Deep Reinforcement Learning (DRL), används för att undersöka möjligheten att maximera CLV för denna typ av data. Studien fann att de bästa RNN-modellerna överträffade den naiva modellen i termer av precision i data där kunder är mer benägna att ändra sin försäkringspolicy. Modellerna lider dock av flera anmärkningsvärda begränsningar, så ytterligare forskning rekommenderas. Att välja kunder med hjälp av LSTM visade sig vara framgångsrikt när det gäller precision men inte känslighet vilket tyder på att det finns utrymme för förbättring. DRL-modellerna visade inte någon väsentlig förbättring vad gäller CLV-maximering.
6
Andréasson, David, and Blomquist Jesper Mortensen. "Forecasting the OMXS30 - a comparison between ARIMA and LSTM." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413793.
Full textAbstract:
Machine learning is a rapidly growing field with more and more applications being proposed every year, including but not limited to the financial sector. In this thesis, historical adjusted closing prices from the OMXS30 index are used to forecast the corresponding future values using two different approaches; one using an ARIMA model and the other using an LSTM neural network. The forecasts are made on three different time intervals: 90, 30 and 7 days ahead. The results showed that the LSTM model performs slightly better when forecasting 90 and 30 days ahead, whereas the ARIMA model has comparable accuracy on the seven day forecast.
7
Xiang, Wenliang. "Anomaly detection by prediction for health monitoring of satellites using LSTM neural networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24695/.
Full textAbstract:
Anomaly detection in satellite has not been well-documented due to the unavailability of satellite data, while it becomes more and more important with the increasing popularity of satellite applications. Our work focus on the anomaly detection by prediction on the dataset from the satellite, where we try and compare performance among recurrent neural network (RNN), Long Short-Term Memory (LSTM) and conventional neural network (NN). We conclude that LSTM with input length p=16, dimensionality n=32, output length q=2, 128 neurons and without maximum overlap is the best in terms of balanced accuracy. And LSTM with p=128, n=32, q=16, 128 and without maximum overlap outperforms most with respect to AUC metric. We also invent award function as a new performance metric trying to capture not only the correctness of decisions that NN made but also the amount of confidence in making its decisions, and we propose two candidates of award function. Regrettably, they partially meet our expectation as they possess a fatal defect which has been proved both from practical and theoretical viewpoints.
8
Korte, Christopher M. "A Preliminary Investigation into using Artificial Neural Networks to Generate Surgical Trajectories to Enable Semi-Autonomous Surgery in Space." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595499765813353.
Full text
9
Martins, Helder. "Predicting user churn on streaming services using recurrent neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217109.
Full textAbstract:
Providers of online services have witnessed a rapid growth of their user base in the last few years. The phenomenon has attracted an increasing number of competitors determined on obtaining their own share of the market. In this context, the cost of attracting new customers has increased significantly, raising the importance of retaining existing clients. Therefore, it has become progressively more important for the companies to improve user experience and ensure they keep a larger share of their users active in consuming their product. Companies are thus compelled to build tools that can identify what prompts customers to stay and also identify the users intent on abandoning the service. The focus of this thesis is to address the problem of predicting user abandonment, also known as "churn", and also detecting motives for user retention on data provided by an online streaming service. Classical models like logistic regression and random forests have been used to predict the churn probability of a customer with a fair amount of precision in the past, commonly by aggregating all known information about a user over a time period into a unique data point. On the other hand, recurrent neural networks, especially the long short-term memory (LSTM) variant, have shown impressive results for other domains like speech recognition and video classification, where the data is treated as a sequence instead. This thesis investigates how LSTM models perform for the task of predicting churn compared to standard nonsequential baseline methods when applied to user behavior data of a music streaming service. It was also explored how different aspects of the data, like the distribution between the churning and retaining classes, the size of user event history and feature representation influences the performance of predictive models. The obtained results show that LSTMs has a comparable performance to random forest for churn detection, while being significantly better than logistic regression. Additionally, a framework for creating a dataset suitable for training predictive models is provided, which can be further explored as to analyze user behavior and to create retention actions that minimize customer abandonment.<br>Leverantörer av onlinetjänster har bevittnat en snabb användartillväxt under de senaste åren. Denna trend har lockat ett ökande antal konkurrenter som vill ta del av denna växande marknad. Detta har resulterat i att kostnaden för att locka nya kunder ökat avsevärt, vilket även ökat vikten av att behålla befintliga kunder. Det har därför gradvis blivit viktigare för företag att förbättra användarupplevelsen och se till att de behåller en större andel avanvändarna aktiva. Företag har därför ett starkt intresse avatt bygga verktyg som kan identifiera vad som driver kunder att stanna eller vad som får dem lämna. Detta arbete fokuserar därför på hur man kan prediktera att en användare är på väg att överge en tjänst, så kallad “churn”, samt identifiera vad som driver detta baserat på data från en onlinetjänst. Klassiska modeller som logistisk regression och random forests har tidigare använts på aggregerad användarinformation över en given tidsperiod för att med relativt god precision prediktera sannolikheten för att en användare kommer överge produkten. Under de senaste åren har dock sekventiella neurala nätverk (särskilt LSTM-varianten Long Short Term Memory), där data istället behandlas som sekvenser, visat imponerande resultat för andra domäner såsom taligenkänning och videoklassificering. Detta arbete undersöker hur väl LSTM-modeller kan användas för att prediktera churn jämfört med traditionella icke-sekventiella metoder när de tillämpas på data över användarbeteende från en musikstreamingtjänst. Arbetet undersöker även hur olika aspekter av data påverkar prestandan av modellerna inklusive distributionen mellan gruppen av användare som överger produkten mot de som stannar, längden av användarhändelseshistorik och olika val av användarfunktioner för modeller och användardatan. De erhållna resultaten visar att LSTM har en jämförbar prestanda med random forest för prediktering av användarchurn samt är signifikant bättre än logistisk regression. LSTMs visar sig således vara ett lämpligt val för att förutsäga churn på användarnivå. Utöver dessa resultat utvecklades även ett ramverk för att skapa dataset som är lämpliga för träning av prediktiva modeller, vilket kan utforskas ytterligare för att analysera användarbeteende och för att skapa förbättrade åtgärder för att behålla användare och minimera antalet kunder som överger tjänsten.
10
Zard, Radjia. "Analyse des données actimétriques et prédiction par LSTM des phases de sommeil chez une population âgée institutionnalisée." Electronic Thesis or Diss., Toulon, 2024. http://www.theses.fr/2024TOUL0009.
Full textAbstract:
Dans cette thèse, nous étudions l’application des réseaux de neurones récurrents (RNN) par LSTM pour la prédiction des phases de sommeil chez des résidents âgés d’un établissement de soin à travers l’analyse des données actimétriques enregistrées. Ce travail est au croisement des domaines médical et de l’informatique dans un contexte d’évolution de l’espérance de vie et du vieillissement de la population. L’objectif principal était d’anticiper le sommeil afin de proposer aux médecins la possibilité d’adapter les prises de traitements et la prise en charge du patient en leur proposant une alternative à la médication. Nous explorons l’application des algorithmes d’apprentissage profond utilisés pour résoudre les problèmes de la prévision des séries temporelles à long et à court terme dans les données séquentielles. Nous avons réalisé une revue systématique afin de construire l’orientation de l’étude. Puis, après l’analyse des enregistrements d’actimétries nous avons utilisé l’architecture LSTM avec une couche cachée avec dropout, puis ajoutée vers une couche entièrement connectée. Cette étape améliore les performances du modèle. Nous avons également traité l’abandon comme une sous-couche du réseau et enfin, la fonction de transfert sigmoïd à tangente hyperbolique appliquée comme fonction d’activation a été utilisée pour calculer la sortie.Nous avons réussi lors de cette étape de recherche à prédire le nombre de phases de sommeil pour un patient âgé institutionnalisé avec les données des enregistrements des 9 nuits précédentes. Nous proposons de poursuivre les recherches avec un échantillon plus grand afin de valider nos résultats<br>In this thesis, we study the application of recurrent neural networks (RNN) by LSTM for the prediction of sleep phases in elderly residents of a care facility through the analysis of recorded actimetric data. This work is at the crossroads of the medical and IT fields, in the context of increasing life expectancy and an aging population. The main objective was to anticipate sleep in order to offer doctors the possibility of adapting treatment and patient management by proposing an alternative to medication. We explore the application of deep learning algorithms used to solve the problems of long- and short-term time series prediction in sequential data. We carried out a systematic review in order to construct the orientation of the study. Then, after analyzing the actimeter records, we used the LSTM architecture with a hidden layer with dropout, then added towards a fully connected layer. This step improves model performance. We also treated the dropout as a sub-layer of the network and finally, the hyperbolic tangent sigmoid transfer function applied as the activation function was used to calculate the output.In this stage of the research, we were able to predict the number of sleep phases for an elderly institutionalized patient using data from recordings of the previous 9 nights. We propose to continue the research with a larger sample in order to validate our results
Books on the topic "RNN LSTM"
1
Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.
Full textAbstract:
Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ
Book chapters on the topic "RNN LSTM"
1
Manaswi, Navin Kumar. "RNN and LSTM." In Deep Learning with Applications Using Python. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3516-4_9.
Full text
2
Das, Susmita, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee. "Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_4.
Full textAbstract:
AbstractRecurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
3
Salem, Fathi M. "Gated RNN: The Long Short-Term Memory (LSTM) RNN." In Recurrent Neural Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89929-5_4.
Full text
4
Kaushik, Anupama, Nisha Choudhary, and Priyanka. "Software Cost Estimation Using LSTM-RNN." In Proceedings of International Conference on Artificial Intelligence and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4992-2_2.
Full text
5
Yadav, Archana, Shivam Kushwaha, Jyoti Gupta, Deepika Saxena, and Ashutosh Kumar Singh. "Cloud Services Management Using LSTM-RNN." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5974-7_13.
Full text
6
Dhandapani, Aarthi, N. Ilakiyaselvan, Satyaki Mandal, Sandipta Bhadra, and V. Viswanathan. "Lyrics Generation Using LSTM and RNN." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1051-9_24.
Full text
7
Dhar, Ankita, Himadri Mukherjee, Sk Md Obaidullah, K. C. Santosh, Niladri Sekhar Dash, and Kaushik Roy. "Web Text Categorization: A LSTM-RNN Approach." In Intelligent Computing and Communication. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_27.
Full text
8
Pawar, Kriti, Raj Srujan Jalem, and Vivek Tiwari. "Stock Market Price Prediction Using LSTM RNN." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2285-3_58.
Full text
9
Shrouti, Devika, Ameysingh Bayas, Nirgoon Joshi, Mrinank Misal, Smita Mahajan, and Shilpa Gite. "Story Generation Using GAN, RNN and LSTM." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56700-1_16.
Full text
10
Shachee, S. B., H. N. Latha, and N. Hegde Veena. "Electrical Energy Consumption Prediction Using LSTM-RNN." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9605-3_25.
Full textConference papers on the topic "RNN LSTM"
1
Ebadinezhad, Sahar, Nooshin Nooraei Nia, Nasratullah Shirzad, and Nwabueze Kenneth Osemeha. "Enhancing Intrusion Detection Systems Using RNN, LSTM, and Hybrid RNN-LSTM Models." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968214.
Full text
2
Kesarwani, Vartika, and Rajesh E. "Stock Market Prediction using LSTM RNN ML." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895287.
Full text
3
Qiu, Junju, and Wei Yang. "Optimization of UAV Intrusion Detection Based on LSTM-RNN." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862571.
Full text
4
Valarmathi, C., and S. John Justin Thangaraj. "Efficient Intrusion Detection Model based on LSTM and RNN." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10941145.
Full text
5
T.R.Ramesh, Kirana Kumar, V. Asha, S. Naveen Kumar, Manish Kumar, and Ali Kareem. "Implementing RNN and LSTM Models to Electrical Load Predictions." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877425.
Full text
6
Gupta, Naman, Siddhant Thapliyal, Akshat Sharma, Jaymin Sheladia, Mohammad Wazid, and Debasis Giri. "Deep Learning Approach for Malicious URL Detection using CNN, RNN, LSTM and Bi-LSTM models." In 2024 6th International Conference on Computational Intelligence and Networks (CINE). IEEE, 2024. https://doi.org/10.1109/cine63708.2024.10881598.
Full text
7
Fathima, Syed Ali, Hariram S, Kanagalingam S. M, Nithish Balaji M, and Muthu Manoj M. "Neural Harmony: Advancing Composition with RNN-LSTM in Music Generation." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649223.
Full text
8
Jenckel, Martin, Syed Saqib Bukhari, and Andreas Dengel. "Training LSTM-RNN with Imperfect Transcription." In HIP2017: The 4th International Workshop on Historical Document Imaging and Processing. ACM, 2017. http://dx.doi.org/10.1145/3151509.3151527.
Full text
9
Srivastava, Priyanka, and P. K. Mishra. "Stock Market Prediction Using RNN LSTM." In 2021 2nd Global Conference for Advancement in Technology (GCAT). IEEE, 2021. http://dx.doi.org/10.1109/gcat52182.2021.9587540.
Full text
10
Mifsud, Yanika, and Frankie Inguanez. "Dance Style Classification by LSTM RNN." In 2021 IEEE 11th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 2021. http://dx.doi.org/10.1109/icce-berlin53567.2021.9720031.
Full textReports on the topic "RNN LSTM"
1
Letcher, Theodore, and Julie Parno. Incorporating advanced snow microphysics and lateral transport into the Noah-Multiparameterization (Noah-MP) land surface model. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47660.
Full textAbstract:
The dynamic state of the land surface presents challenges and opportunities for military and civil operations in extreme cold environments. In particular, the effects of snow and frozen ground on Soldier and vehicle mobility are hard to overstate. Current authoritative weather and land models are run at global scales (i.e., dx > 10 km) and are of limited use at the Soldier scale (dx < 100 m). Here, we describe several snow physics upgrades made to the Noah-Multiparameterization (Noah-MP) community land surface model (LSM). These upgrades include a blowing snow overlay to simulate the lateral redistribution of snow by the wind and the addition of new prognostic snow microstructure variables, namely grain size and bond radius. These additions represent major upgrades to the snow component of the Noah-MP LSM because they incorporate processes and methods used in more specialized snow modeling frameworks. These upgrades are demonstrated in idealized and real-world applications. The test simulations were promising and show that the newly added snow physics replicate observed behavior with reasonable accuracy. We hope these upgrades facilitate ongoing and future research on characterizing the effects of the integrated snow and soil land surface in extreme cold environments at the tactical scale.