Relevant bibliographies by topics / LSTM

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'LSTM'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 March 2025

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Journal articles on the topic "LSTM"

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Murugesan, R., Eva Mishra, and Akash Hari Krishnan. "Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM." International Journal of Sustainable Agricultural Management and Informatics 8, no. 3 (2022): 242. http://dx.doi.org/10.1504/ijsami.2022.125757.

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Krishnan, Akash Hari, R. Murugesan, and Eva Mishra. "Forecasting agricultural commodities prices using deep learning-based models: basic LSTM, bi-LSTM, stacked LSTM, CNN LSTM, and convolutional LSTM." International Journal of Sustainable Agricultural Management and Informatics 8, no. 3 (2022): 1. http://dx.doi.org/10.1504/ijsami.2022.10048228.

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Roberg, Kevin J., Stephen Bickel, Neil Rowley, and Chris A. Kaiser. "Control of Amino Acid Permease Sorting in the Late Secretory Pathway of Saccharomyces cerevisiae by SEC13, LST4, LST7 and LST78." Genetics 147, no. 4 (December 1, 1997): 1569–84. http://dx.doi.org/10.1093/genetics/147.4.1569.

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Abstract The SEC13 gene was originally identified by temperature-sensitive mutations that block all protein transport from the ER to the Golgi. We have found that at a permissive temperature for growth, the sec13-1 mutation selectively blocks transport of the nitrogen-regulated amino acid permease, Gaplp, from the Golgi to the plasma membrane, but does not affect the activity of constitutive permeases such as Hip1p, Can1p, or Lyp1p. Different alleles of SEC13 exhibit different relative effects on protein transport from the ER to the Golgi, or on Gap1p activity, indicating distinct requirements for SEC13 function at two different steps in the secretory pathway. Three new genes, LST4, LST7, and LSTB, were identified that are also required for amino acid permease transport from the Golgi to the cell surface. Mutations in LST4 and LST7 reduce the activity of the nitrogen-regulated permeases Gap1p and Put4p, whereas mutations in LST8 impair the activities of a broader set of amino acid permeases. The LST8 gene encodes a protein composed of WD-repeats and has a close human homologue. The LST7 gene encodes a novel protein. Together, these data indicate that SEC13, LST4, LST7, and LST8 function in the regulated delivery of Gap1p to the cell surface, perhaps as components of a post-Golgi secretory-vesicle coat.
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Victor, Nancy, and Daphne Lopez. "sl-LSTM." International Journal of Grid and High Performance Computing 12, no. 3 (July 2020): 1–16. http://dx.doi.org/10.4018/ijghpc.2020070101.

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The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.
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Liu, Zhandong, Wengang Zhou, and Houqiang Li. "AB-LSTM." ACM Transactions on Multimedia Computing, Communications, and Applications 15, no. 4 (January 10, 2020): 1–23. http://dx.doi.org/10.1145/3356728.

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Suebsombut, Paweena, Aicha Sekhari, Pradorn Sureephong, Abdelhak Belhi, and Abdelaziz Bouras. "Field Data Forecasting Using LSTM and Bi-LSTM Approaches." Applied Sciences 11, no. 24 (December 13, 2021): 11820. http://dx.doi.org/10.3390/app112411820.

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Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.
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Liang, Xinyue. "Stock Market Prediction with RNN-LSTM and GA-LSTM." SHS Web of Conferences 196 (2024): 02006. http://dx.doi.org/10.1051/shsconf/202419602006.

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The stock price reflects various factors such as the rate of economic growth, inflation, overall economy, trade balance, and monetary system, all of which impact the stock market as a whole. Investors often find the principle of stock price trends unclear because of the many important variables involved. When creating an investment strategy or determining the timing for buying or selling stocks, forecasting stock market trends plays a critical role. It is difficult to estimate the value of the stock market due to the non-linear and dynamic nature of the stock index. Numerous studies using deep learning techniques have been successful in making such predictions. The Long Short Term Memory (LSTM) has become popular for predicting stock market prices. This paper thoroughly examines methods for predicting stock market performance using RNN-LSTM and GA-LSTM, provides explanations of these methods, and performs a comparative analysis. We will discuss future directions and outline the significance of using RNN-LSTM and GA-LSTM for forecasting stock market trends, based on the papers we have reviewed.
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Song, Jun, Siliang Tang, Jun Xiao, Fei Wu, and Zhongfei Zhang. "LSTM-in-LSTM for generating long descriptions of images." Computational Visual Media 2, no. 4 (November 15, 2016): 379–88. http://dx.doi.org/10.1007/s41095-016-0059-z.

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Nilasari, Ni Ketut Novia, Made Sudarma, and Nyoman Gunantara. "Prediksi Nilai Cryptocurrency Dengan Metode Bi-LSTM dan LSTM." Majalah Ilmiah Teknologi Elektro 22, no. 2 (December 19, 2023): 221. http://dx.doi.org/10.24843/mite.2023.v22i02.p09.

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Semakin pesatnya perkembangan teknologi saat ini, dapat memudahkan seluruh kegiatan manusia, sehingga mengakibatkan seluruh aspek tidak bisa lepas dari teknologi tanpa terkecuali bidang keuangan. Dengan berkembangnya teknologi diiringi juga dengan dikenalnya berbagai instrument investasi. Setiap melaksanakan investasi tentu akan selalu ada berbagai resiko yang menyertainya termasuk investasi cryptocurrency salah satunya bitcoin. Tidak seperti mata uang konvensional, bitcoin bersifat tidak desentralisasi sehingga perkembangan harganya tidak dalam pengawasan atau kontrol pihak manapun, dimana jika uang konvensional ada lembaga tertentu yang mengawasi dan mengontrol pergerakannya. Hal tersebut mengakibatkan harga nilai tukar dari bitcoin menjadi tidak konsisten atau tidak stabil. Dengan terdapatnya metode prediksi, pengguna bitcoin bisa menetapkan waktu yang pas untuk menjalankan transaksi. Penelitian ini memiliki tujuan guna memprediksi harga bitcoin dengan menggunakan metode LSTM serta Bi-LSTM. Berdasarkan hasil penelitian diperoleh hasil prediksi terbaik menggunakan metode Bi-LSTM dengan RMSE 1482.73 sedangkan dengan LSTM menghasilkan RMSE sebesar 1768.69 sehingga dapat disimpulkan dari sisi akurasi Bi-LSTM memberikan hasil yang lebih akurat hanya saja dengan Bi-LSTM membutuhkan resourse yang lebih banyak.
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Zhang, Xinchen, Linghao Zhang, Qincheng Zhou, and Xu Jin. "A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model." Computational Intelligence and Neuroscience 2022 (May 5, 2022): 1–12. http://dx.doi.org/10.1155/2022/1643413.

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As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.
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Dissertations / Theses on the topic "LSTM"

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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.

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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio files, the conventionalLSTM network yielded an accuracy rate of 64.8 % while the grid LSTM network yielded anaccuracy rate of 65.2 %. Statistically, there was no difference in the accuracy rate betweenthe models. In addition, the conventional LSTM network took 2 % longer to train. However,this difference in training time is considered to be of little significance when tralnslating it toabsolute time. Thus, it can be concluded that the one-dimensional grid LSTM model performsjust as well as the conventional one.
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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.

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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.

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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean position of the thunderstorms using an LSTM network. A random search is then carried out to identify optimal parameters for the LSTM model. It is shown that the trajectories predicted by the LSTM are much closer to the true trajectories than what a linear model predicts. This is especially true for predictions of more than 1 hour. Scores commonly used to measure forecast accuracy are applied to compare the LSTM and linear model. It is found that the LSTM significantly improves forecast accuracy compared to the linear model.
Åskväder är både farliga och livsviktiga bärare av vatten för stora delar av världen. Det är dock svårt att förutsäga åskcellernas banor, främst i tropiska områden. Detta beror till större delen på deras mindre storlek och kortare livslängd. Detta examensarbete undersöker hur väl ett neuralt nätverk, bestående av long short-term memory-lager (LSTM) kan förutsäga åskväders banor baserat på flera års blixtnedlslagsdata. Först klustras datan, och viktiga karaktärsdrag hämtas ut från den. Dessa används för att förutspå åskvädrens genomsnittliga position med hjälp av ett LSTMnätverk. En slumpmässig sökning genomförs sedan för att identifiera optimala parametrar för LSTM-modellen. Det fastslås att de banor som förutspås av LSTM-modellen är mycket närmare de sanna banorna, än de som förutspås av en linjär modell. Detta gäller i synnerhet för förutsägelser mer än 1 timme framåt. Värden som är vanliga för att bedöma prognosers träffsäkerhet beräknas för att jämföra LSTM-modellen och den linjära. Det visas att LSTM-modellen klart förbättrar förutsägelsernas träffsäkerhet jämfört med den linjära modellen.
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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.

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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. This is important since the handling training data can enormously influence on the final prediction accuracy of the network. Finally, a number of changes to improve these methods are suggested. These include ways to raise accuracy, reduce network overhead, and to improve the consistency of results. This work shows that augmenting an LSTM prefetcher with a simple stream prefetcher leads to moderate improvements in prediction accuracy. This could be a way to start reducing the size of neural networks so they are usable in real hardware.
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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.

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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substituting active performance measurements with a machine-learning algorithm, you reduce the overhead created by active performance measurements Previous research has revolved around applying traditional machine learning algorithms to wireless sensor network features such as packet statistics and topological information of the network to predict round-trip time. The purpose of this thesis is to use a  more advanced deep learning algorithm namely Long short-term memory (LSTM) to try and exploit time dependencies in the data Three different datasets containing network statistics are used in three different experiments. In every experiment, LSTM models with different configurations are created, and their predictioncapabilities are compared to traditional neural networks with equivalent configurations. In all experiments, both the LSTM model and its corresponding equivalent neural network model produced similar results, meaning that a time dependency in the data could not be proven.
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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.

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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas och evalueras samtidigt. Agenterna utvecklades med hjälp av en genetisk algoritm. Resultaten visade att LSTM var minst lika bra som feedforward men båda metoderna blev slagna av Machados metoder. Toppoängen i varje spel jämfördes med Granfelts arbete som har varit en utgångspunkt för detta arbete.
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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.
LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.

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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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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.

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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.
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.
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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone.
Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
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Books on the topic "LSTM"

1

Ecip, S. Sinansari. LSM sariawan? Jakarta: Pustaka Firdaus, 1995.

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Long, Tùng. Muot lsan lsam lzo. TP. HCM [i.e. Thành phro Hso Chí Minh]: NXB Văn nghue, 2008.

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Van, Sona. Es dzayn em lsum. Erevan: Graber, 2006.

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Finanzen, Austria Bundesministerium für. Lohnsteuerrichtlinien 1999: LStR 1999. 2nd ed. Wien: P. Dorner, 1999.

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Trung, Sĩ. Muot lsan lsam lzo: Truyuen. Paris: Nam Á, 1986.

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Oklahoma. Dept. of Libraries. LSTA 5-year plan, 2003-2007. Oklahoma City, OK: Oklahoma Dept. of Libraries, 2002.

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Lang, Trọng. Hà Nuoi lsam than: Phóng svu. Los Alamitos, CA: Xuân Thu, 1990.

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Corporation, Loan Pricing. LSTA/LPC mark-to market pricing. [New York, NY]: Loan Pricing Corporation, 2005.

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Bambang, Ismawan, Pamuji Otok S, and Bina Swadaya (Organization), eds. LSM dan program IDT. Jakarta: Bina Swadaya, 1994.

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D'Orta, Marcello. [In Afrika lst immer August]. [Tehran?]: [S.n.], 2000.

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Book chapters on the topic "LSTM"

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Korstanje, Joos. "LSTM RNNs." In Advanced Forecasting with Python, 243–51. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7150-6_18.

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Eelbode, Tom, Pieter Sinonquel, Raf Bisschops, and Frederik Maes. "Convolutional LSTM." In Computer-Aided Analysis of Gastrointestinal Videos, 121–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64340-9_14.

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Wang, Ximin, Luyi Huang, Junlan Zhu, Wenbo He, Zhaopeng Qin, and Ming Yuan. "LSTM-Exploit: Intelligent Penetration Based on LSTM Tool." In Advances in Artificial Intelligence and Security, 84–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78615-1_8.

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Manaswi, Navin Kumar. "RNN and LSTM." In Deep Learning with Applications Using Python, 115–26. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3516-4_9.

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Adam, Kazybek, Kamilya Smagulova, and Alex Pappachen James. "Memristive LSTM Architectures." In Modeling and Optimization in Science and Technologies, 155–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14524-8_12.

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Yee-King, Matthew John. "JUCE LSTM plugin." In Build AI-Enhanced Audio Plugins with C++, 274–86. London: Focal Press, 2024. http://dx.doi.org/10.4324/9781003365495-37.

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Tuna, Rodrigo, Yassine Baghoussi, Carlos Soares, and João Mendes-Moreira. "Kernel Corrector LSTM." In Lecture Notes in Computer Science, 3–14. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58553-1_1.

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Ninagawa, Chuzo. "LSTM AI Modeling." In AI Time Series Control System Modelling, 67–90. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4594-6_4.

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Al-Qerem, Ahmad, Mohammed Raja, Sameh Taqatqa, and Mutaz Rsmi Abu Sara. "Utilizing Deep Learning Models (RNN, LSTM, CNN-LSTM, and Bi-LSTM) for Arabic Text Classification." In Studies in Systems, Decision and Control, 287–301. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43490-7_22.

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Huynh, Manh, and Gita Alaghband. "Trajectory Prediction by Coupling Scene-LSTM with Human Movement LSTM." In Advances in Visual Computing, 244–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33720-9_19.

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Conference papers on the topic "LSTM"

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Togatorop, Andrew Reinhard Marulak, and Mohammad Isa Irawan. "Nickel Price Prediction Using Bi-Directional LSTM (Bi-LSTM) and Attention Bi-Directional LSTM Network (At-Bi-LSTM)." In 2024 IEEE International Symposium on Consumer Technology (ISCT), 450–56. IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791230.

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Oguzie, Goodluck. "Cosine-Gated LSTM." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML), 8–15. IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779680.

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Wang, Yufei. "Stock Price Prediction Based on CNN, LSTM and CNN- LSTM Model." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy, 22–30. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012982600004601.

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Fadili, Yousra, Yassine El Yamani, Jihad Kilani, Najib El Kamoun, Youssef Baddi, and Faycal Bensalah. "An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-LSTM Architectures." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10655101.

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Özer, Serpil, and Ümmühan Nida Erol. "Electric Vehicle Stock Price Prediction using LSTM, Bi-LSTM and GRU." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710919.

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Abbasi, Mahsa, Masoud Kargar, Fatemeh Ahmadian, Deniz NoormohammadZadehMaleki, Amirhossein Arandan, and Nayer Seyed Hosseini. "GN-CNN-LSTM: Financial Market Prediction With Gaussian Noise Embedded CNN LSTM." In 2024 11th International Symposium on Telecommunications (IST), 287–94. IEEE, 2024. https://doi.org/10.1109/ist64061.2024.10843452.

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Vaka, Srikar, M. Sudarshan Reddy, and S. Nagendra Prabhu. "Hybrid Model for Cryptocurrency Price Prediction using LSTM, Bidirectional LSTM, and XGBoost." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), 925–32. IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823223.

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Mondal, Pronoy Kumar, Sadman Sadik Khan, Md Toufiq Imrog, Md Ainul Ahsan Arman, Md Muhetul Islam, and Afraz Ul Haque Rupak. "Exploring Authorial Style in Bangla Literature: LSTM and Bi-LSTM -Based Author Detection." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–9. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725023.

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Han, Khin Su Su, and May Thu Myint. "A Comparative Study of LSTM, Bi-LSTM, and BERT for Automated Essay Scoring." In 2024 5th International Conference on Advanced Information Technologies (ICAIT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icait65209.2024.10754925.

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Tang, Xinxiong. "Optimizing LSTM, Bi-LSTM, and GRU Models with SSA for Daily Electricity Forecasting." In 2024 5th International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI), 53–58. IEEE, 2024. https://doi.org/10.1109/ichci63580.2024.10808092.

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Reports on the topic "LSTM"

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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, June 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the other LSTM application and ARIMA models optimized for forecasting (with and without explanatory variables). This improvement in forecasting accuracy is most pronounced over longer time horizons, specifically from the seventh month onwards.
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Murugan, Venkatachalam, and Jeyaswamidoss Jeba Emilyn. Monitoring and Forecasting of Water Quality and Fish Population Using Stacked LSTM-GRU in IOT Environment. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, October 2021. http://dx.doi.org/10.7546/crabs.2021.10.13.

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Fili, Mohammad. Predicting the Number of New COVID-19 Cases using an LSTM-based Model for European Countries. Ames (Iowa): Iowa State University, August 2022. http://dx.doi.org/10.31274/cc-20240624-1144.

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Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1760289.

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Hepworth, Nick. Reading Pack: Tackling the Global Water Crisis: The Role of Water Footprints and Water Stewardship. Institute of Development Studies (IDS), August 2021. http://dx.doi.org/10.19088/k4d.2021.109.

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The K4D professional development Reading Packs provide thought-provoking introductions by international experts and highlight the emerging issues and debates within them. They aim to help inform policies that are more resilient to the future. K4D services are provided by a consortium of leading organisations working in international development, led by the Institute of Development Studies (IDS), with the Education Development Trust, Itad, University of Leeds Nuffield Centre for International Health and Development, Liverpool School of Tropical Medicine (LSTM), University of Birmingham International Development Department (IDD) and the University of Manchester Humanitarian and Conflict Response Institute (HCRI). For any enquiries, please contact helpdesk@k4d.info
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Kuo, Meng-Hsuan, Chih-Wei Tseng, Ching-Sheng Hsu, Yen-Chun Chen, I.-Ting Kao, and Chen-Yi Wu. Protocol for systematic review and meta-analysis of prognostic value of sarcopenia in advanced HCC patients treating with systemic therapy. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0011.

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Review question / Objective: P: Advanced HCC patients under systemic therapy; I: low skeletal muscle mass (LSMM); C: Non-LSMM; O:overall survival or mortality. Eligibility criteria: (1) cohort studies or cross sectional studies investigations with HCC patients treated with systemic therapy; (2) the articles estimated pretreatment skeletal muscle mass measured by CT-images; (3) studies provided statistical data about the prevalence pretreatment LSMM or influence of LSMM on OS orPFS.
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Williams, N. Characterization of LST Z Plane Signals. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/833108.

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Lee, S., B. D. Chung, and H. J. Kim. RELAP5 assessment using LSTF test data SB-CL-18. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10162957.

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Palmer, Jennifer, and Diane Duclos. Key Considerations: Community-Based Surveillance in Public Health. Institute of Development Studies, May 2023. http://dx.doi.org/10.19088/sshap.2023.010.

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Recent large-scale epidemics and pandemics have demonstrated the importance of engaging communities as partners in preventing, detecting and responding to public health emergencies. Community-based surveillance (CBS), which relies on communities to report public health information, can be an important part of effective, inclusive and accountable responses to humanitarian and public health emergencies, as well as long-term disease control. This brief offers key considerations for CBS programming to guide policymakers, public health officials, civil society organisations, health workers, researchers, advocates, and others interested in health surveillance. It is based on a rapid review of CBS guidance and social science literature. It was written by Jennifer Palmer and Diane Duclos (both London School of Hygiene & Tropical Medicine, LSHTM) with contributions by Mariam Sharif (École des Hautes Études en Sciences Sociales, EHESS). It was reviewed by Ruwan Ratnayake (LSHTM), Maysoon Dahab (LSHTM) and Luisa Enria (LSHTM). This brief is the responsibility of the Social Science in Humanitarian Action Platform (SSHAP).
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