Дисертації з теми "LSTM ALGORITHM"
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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.
Повний текст джерелаLSTM 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.
Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.
Повний текст джерелаMalina, Ondřej. "Detekce začátku a konce komplexu QRS s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442595.
Повний текст джерелаOlsson, Charlie, and David Hurtig. "An approach to evaluate machine learning algorithms for appliance classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20217.
Повний текст джерелаFreberg, Daniel. "Evaluating Statistical MachineLearning and Deep Learning Algorithms for Anomaly Detection in Chat Messages." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235957.
Повний текст джерелаAtt automatiskt kunna upptäcka anomalier i text har stora implikationer för företag och myndigheter som övervakar olika sorters kommunikation. I detta examensarbete utvärderas tre olika maskininlärningsalgoritmer för chattmeddelandeklassifikation i ett marknadsövervakningsystem. Naive Bayes och Support Vector Machine tillhör båda den statistiska klassen av maskininlärningsalgoritmer som utvärderas i studien och bådar kräver selektion av vilka särdrag i texten som ska användas i algoritmen. Ett sekundärt mål med studien är således att hitta en passande selektionsteknik för att de statistiska algoritmerna ska prestera så bra som möjligt. Long Short-Term Memory Network är djupinlärningsalgoritmen som utvärderas i studien. Istället för att använda en selektionsteknik kommer djupinlärningsalgoritmen nyttja ordvektorer för att representera text. Resultaten visar att alla utvärderade algoritmer kan nå hög prestanda för ändamålet, i synnerhet Naive Bayes tillsammans med termfrekvensselektion.
Almqvist, Olof. "A comparative study between algorithms for time series forecasting on customer prediction : An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16974.
Повний текст джерелаBlanco, Martínez Alejandro. "Study and design of classification algorithms for diagnosis and prognosis of failures in wind turbines from SCADA data." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/586097.
Повний текст джерелаNowadays, the preventive maintenance operations of wind farms are supported by Machine Learning techniques to reduce the costs of unplanned downtime. That is why an early fault prediction that works with SCADA data is required. These data need to be processed at different stages described in this thesis, with results published in each of them. In a first phase, the extreme values (Outliers) are cleaned, indicating how they should address in order not to eliminate the information about the faults. In a second step, the different variables are selected by different Feature Selection methods. At the same step, the use of variables transformed by Autoencoders is also compared. In a third, the model is constructed using Supervised and Unsupervised methods, obtaining outstanding results with Self Organizing Maps (SOM) and Deep Learning techniques including ANN and LSTM multi-layer networks.
Actualment les operacions de manteniment preventiu dels parcs eòlics se suporten sobre tècniques de Machine Learning per a reduir els costos de les parades no planificades. Per això es necessita una predicció de fallades amb certa anticipació que funcioni sobre les dades de SCADA. Aquestes dades necessiten ser processades en diferents etapes descrites a aquesta tesi, amb resultats publicats en cadascuna d'elles. En una primera fase es netegen els valors extrems (Outliers), indicant com han de ser tractats per no eliminar la informació sobre les fallades. En una segona, les diferents variables són seleccionades per diversos mètodes de selecció de característiques (Feature Selection). En la mateixa fase, es compara l'ús de variables transformades mitjançant Autoencoders. En una tercera es construeix el model, mitjançant mètodes supervisats i no supervisats, obtenint resultats destacables amb Self Organizing Maps (SOM) i amb tècniques de Deep Learning incloent xarxes ANN i LSTM multicapa.
Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.
Повний текст джерелаPrediktion av den finansiella marknadens beteende är i stort ett olöst problem. Problemet hartagits an på flera sätt med olika metoder så som binär logik, statistiska uträkningar ochgenetiska algoritmer. I den här uppsatsen kommer problemet undersökas medmaskininlärning, mer specifikt Long Short-Term Memory (LSTM), en variant av rekurrentaneurala nätverk (RNN). Rekurrenta neurala nätverk är en typ av artificiellt neuralt nätverk(ANN), en maskininlärningsalgoritm som ska efterlikna de neurala processerna hos däggdjursnervsystem, specifikt utformat för tidsserier. I uppsatsen undersöks kapaciteten hos ett LSTMatt modellera finansmarknadens beteenden och jämförs den mot ett traditionellt RNN, merspecifikt mäts deras effektivitet på olika vis.
Nitz, Pettersson Hannes, and Samuel Vikström. "VISION-BASED ROBOT CONTROLLER FOR HUMAN-ROBOT INTERACTION USING PREDICTIVE ALGORITHMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54609.
Повний текст джерелаAlsulami, Khalil Ibrahim D. "Application-Based Network Traffic Generator for Networking AI Model Development." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354.
Повний текст джерелаMohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаLousseief, Elias. "MahlerNet : Unbounded Orchestral Music with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264993.
Повний текст джерелаMatematik och statistik i allmänhet, och maskininlärning och neurala nätverk i synnerhet, har sedan långt tillbaka använts för att modellera musik med en utveckling som kulminerat under de senaste decennierna. Exakt vid vilken historisk tidpunkt som musikalisk komposition för första gången tillämpades med strikt systematiska regler är svårt att säga; vissa skulle hävda att det skedde under Mozarts dagar, andra att det skedde redan långt tidigare. Oavsett vilket, innebär det att systematisk komposition är en företeelse med lång historia. Även om kompositörer i alla tider följt strukturer och regler, medvetet eller ej, som en del av kompositionsprocessen började man under 1900-talets mitt att göra detta i högre utsträckning och det var också då som de första programmen för musikalisk komposition, baserade på matematik, kom till. Den här uppsatsen i datateknik behandlar hur musik historiskt har komponerats med hjälp av datorer, ett område som också är känt som algoritmisk komposition. Uppsatsens fokus ligger på användning av maskininlärning och neurala nätverk och består av två delar: en litteraturstudie som i hög detalj behandlar utvecklingen under de senaste decennierna från vilken tas inspiration och erfarenheter för att konstruera MahlerNet, ett neuralt nätverk baserat på de tidigare modellerna MusicVAE, BALSTM, PerformanceRNN och BachProp. MahlerNet kan modellera polyfon musik med upp till 23 instrument och är en ny arkitektur som kommer tillsammans med en egen preprocessor som använder heuristiker från musikteori för att normalisera och filtrera data i MIDI-format till en intern representation. MahlerNet, och dess preprocessor, är helt och hållet implementerade för detta arbete och kan komponera musik som tydligt uppvisar egenskaper från den musik som nätverket tränats på. En viss kontinuitet finns i den skapade musiken även om det inte är i form av konkreta teman och motiv.
Liu, Szu-Yu, and 劉思妤. "Using LSTM algorithm to improve network management in SDN." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qqh5m3.
Повний текст джерела國立交通大學
資訊管理研究所
107
There are a lot of network monitoring technologies existed so far. Network administrators must have accurate monitoring to operate efficiently. In this paper, we propose a dynamic adjustment threshold method – Long short term memory network. In a resource-constrained network, SDN traffic engineering (SDN TE) can improve network utilization and service quality. we use a minimum bandwidth utilization routing mechanism to avoid congestion. The controller periodically monitors the traffic utilization of each link in the network. The overused links are identified as a bottleneck link. Removing the bottleneck links by the utilization rate, the remaining bandwidth calculation to be passed by the routing algorithm becomes the alternate selection path. When network traffic increases, the proposed dynamic adjustment utilization method - long-term and short-term memory networks can effectively predict traffic and improve network efficiency and network service quality.
AGGARWAL, TUSHAR. "IMAGE DESCRIPTIVE SUMMARIZATION BY DEEP LEARNING AND ADVANCED LSTM MODEL ARCHITECTURE." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17084.
Повний текст джерелаTsai, Jia-Ling, and 蔡佳陵. "A LSTM-Based Algorithm for the Estimation of Plantar Pressure Dynamics Using Inertial Sensors." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f9dsxf.
Повний текст джерела國立清華大學
資訊工程學系所
106
Gait analysis has become prevalent in many fields such as sports biomechanics, medical diagnostics, and injury prevention. For the plantar pressure dynamic estimation in gait analysis, the vertical component of ground reaction force (vGRF) and center of pressure (CoP) trajectories are vital parameters about human locomotion and balance. Three main approaches are used in measuring the gait parameters, namely computer vision, floor sensors, and wearables. Though the first two techniques are accurate, they are expensive and limited in the sensing area. The wearable devices have advantages in portable and low cost. However, the contact sensing like pressure detector suffers from long-term reliability. In this research, we employ the 6DOF inertial measurement unit (IMU) (gyro system, Taiwan) to estimate the vGRF and CoP trajectories. Unlike conventional pressure sensing, IMUs are low cost and durable. Four IMUs are attached on the heel of two feet, left shank, and waist. F-scan pressure sensing system serves as the ground truth. Associated with hardware setup, we propose a LSTM model to predict vGRF and CoP, based on the acceleration and angle velocity data from IMUs. The data synchronization, data formation, and LSTM structure are explained. Under 33,880 sample points of normal walks, the first 70% is for training and the last 30% is for testing. Experiment shows the root mean square error of peak value of vGRF is equal to 4.83N, the error is 4.025% and the root mean square error of CoP excursion is 0.14cm.
Oguntala, George A., Yim Fun Hu, Ali A. S. Alabdullah, Raed A. Abd-Alhameed, Muhammad Ali, and D. K. Luong. "Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living." 2021. http://hdl.handle.net/10454/18418.
Повний текст джерелаIEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers pervasive sensing environment for the elderly, persons with disability and chronic illness.
Tseng, Xian-Hong, and 曾憲泓. "Using LSTM algorithm to establish an Evaluation System for Child with Autistic Disorder during Autism Diagnostic Observation Schedule Interview." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/nr83hf.
Повний текст джерела國立清華大學
電機工程學系所
106
Autism spectrum disorder (ASD) is a highly-prevalent neuraldevelopmental disorder. In medical research often characterized by social communicative deficits and restricted repetitive interest. The heterogeneous nature of ASD in its behavior manifestations encompasses broad syndromes such as, Classical Autism (AD), Asperger syndrome (AS), and High functioning Autism (HFA). To evaluate the degree and there syndromes in ASD, doctor will diagnose through clinical observation and auxiliary diagnostic tools, one of them is Autism Diagnostic Observation Schedule (ADOS), i.e., a gold standard diagnostic tool. However, there are existing some problems in diagnosis of autism such as, subjective evaluation, non-scalable, and time-consuming. In this work, we design an automatic assessment system based on computing multimodal behavior features, including acoustic characteristic、body movements of the participant, using LSTM algorithm and machine learning technique to build model during ADOS story-telling part by behavioral signal processing (BSP) concept. Further, our behavior-based measurement achieve competitive, sometimes exceeding, recognition accuracies in discriminating between three syndromes of ASD when compare to investigator’s clinical-rating on participant during ADOS. Keywords: autism spectrum disorder, autism diagnostic observation schedule, long short-term memory, behavioral signal processing (BSP), multimodal behaviors
CHU, YI-JUI, and 朱奕叡. "A Study of PCA dimensionality reduction technique and news articles for the Prediction of Stock Price - Using LSTM algorithm as modeling technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nz4at6.
Повний текст джерела輔仁大學
資訊管理學系碩士在職專班
107
The main purpose of this study is to examine the influence of different data structure attributes, the number of eigenvalues, and the length of the training period on the accuracy of individual stock price trend prediction. In this study, the deep learning technique, of the LSTM algorithm, is used as the predictive model, and the PCA algorithm is adopted to screen the technical indicators for dimensionality reduction, moreover, Word2Vect text search technology is used to process the non-structural data of stock news, which is adopted as the predicted feature value of the LSTM model. Situational experiments are based on testing their impact on individual stock price trend forecasts. The experimental results show that: Firstly, the performance obtained by the eigenvalues of the training period of 50 days is better than the performance of the eigenvalues of the training period of 60 days, and consequently the training period of the appropriate length is significant. Secondly, more prediction models used in the number of eigenvalues, may not necessarily generate better prediction results, and using PCA to filter eigenvalues can indeed improve the prediction accuracy. Thirdly, using the rising lexicon in unstructured data to obtain news scores, the eigenvalue prediction accuracy is higher than the prediction accuracy of the eigenvalues in the news vector. And in TSMC (2317) and Foxconn (2330), both grades of the ticket test demonstrate a good accuracy rate of 79.59% and 78.95%. Finally, unstructured news scores combined with structured and dimensionally reduced technical indicators best predict the rise and fall of individual stocks. The experimental targets present a high accuracy of nearly 90% that meets the standard for actual trading practice, within online trading applications.
Chen, Brian, and 陳柏穎. "AUC oriented Bidirectional LSTM-CRF Models to Identify Algorithms Described in an Abstract." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p3grat.
Повний текст джерела國立臺灣大學
資訊工程學研究所
105
In this thesis, we attempt to identify algorithms mentioned in the paper abstract. We further want to discriminate the algorithm proposed in this paper from algorithms only mentioned or compared, since we are more interested in the former. We model this task as a sequential labeled task and propose to use a state-of-the-art deep learning model LSTM-CRF as our solution. However, the data or labels are generally imbalanced since not all the sentence in the abstract is describing its algorithm. That is, the ratio between different labels is skewed. As a result, it is not suitable to use traditional LSTM-CRF model since it only optimizes accuracy. Instead, it is more reasonable to optimize AUC in imbalanced data because it can deal with skewed labels and perform better in predicting rare labels. Our experiment shows that the proposed AUC-optimized LSTM-CRF outperforms the traditional LSTM-CRF. We also show the ranking of algorithms used currently, and find the trend of different algorithms used in recent years. Moreover, we are able to discover some new algorithms that do not exist in our training data.
Kurach, Karol. "Deep Neural Architectures for Algorithms and Sequential Data." Doctoral thesis, 2016. https://depotuw.ceon.pl/handle/item/1860.
Повний текст джерелаPierwsza część pracy przedstawia dwie głębokie architektury neuronowe wykorzystujące pamięć zewnętrzną: Neural Random-Access Machine (NRAM) oraz Hierarchical Attentive Memory (HAM). Pomysł na architekturę NRAM jest inspirowany Neuronowymi Maszynami Turinga (NTM). NRAM, w przeciwieństwie do NTM, posiada mechanizmy umożliwiające wykorzystanie wskaźników do pamięci. To sprawia, że NRAM jest w stanie nauczyć się pojęć wymagających użycia wskaźników, takich jak „lista jednokierunkowa” albo „drzewo binarne”. Architektura HAM bazuje na pełnym drzewie binarnym, w którym liście odpowiadają elementom pamięci. Umożliwia to wykonywanie operacji na pamięci w czasie Θ(log n), co jest znaczącą poprawą względem dostępu w czasie Θ(n), standardowo używanym w implementacji mechanizmu „skupienia uwagi” (ang. attention) w sieciach rekurencyjnych. Pokazujemy, że sieć LSTM połączona z HAM jest w stanie rozwiązać wymagające zadania o charakterze algorytmicznym. W szczególności, jest to pierwsza architektura, która mając dane jedynie pary wejście/poprawne wyjście potrafi się nauczyć sortowania elementów działającego w złożoności Θ(n log n) i dobrze generalizującego się do dłuższych ciągów. Pokazujemy również, że HAM jest ogólną architekturą, która może zostać wytrenowana aby działała jak standardowe struktury danych, takie jak stos, kolejka lub kolejka priorytetowa. Druga część pracy przedstawia trzy nowatorskie systemy bazujące na głębokich sieciach neuronowych. Pierwszy z nich to system do znajdowania wydajnych obliczeniowo formuł matematycznych. Przy wykorzystaniu sieci rekursywnej system jest w stanie efektywnie przeszukiwać przestrzeń stanów i szybko znajdować tożsame formułyo istotnie lepszej złożoności asymptotycznej (przykładowo, Θ(n^2) zamiast złożoności wykładniczej). Następnie, prezentujemy oparty na rekurencyjnej sieci neuronowej system do przewidywania niebezpiecznych zdarzeń z wielowymiarowych, niestacjonarnych szeregów czasowych. Nasza metoda osiągnęła bardzo dobre wyniki w dwóch konkursach uczenia maszynowego. Jako ostatni opisany został Smart Reply – system do sugerowania automatycznych odpowiedzi na e-maile. Smart Reply został zaimplementowany w Google Inbox i codziennie przetwarza setki milionów wiadomości. Aktualnie, 10% wiadomości wysłanych z urządzeń mobilnych jest generowana przez ten system.
JHA, ROMAN KUMAR. "FORECASTING OF SOLAR IRRADIATION USING DEEP LEARNING ALGORITHMS." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19274.
Повний текст джерелаPeterson, Cole. "Generating rhyming poetry using LSTM recurrent neural networks." Thesis, 2019. http://hdl.handle.net/1828/10801.
Повний текст джерелаGraduate
Lopes, Tiago Miguel Dias da Gama Lobo de Sousa. "Como construir um modelo híbrido de previsão para o S&P500 usando um modelo VECM com um algoritmo LSTM?" Master's thesis, 2021. http://hdl.handle.net/10071/23512.
Повний текст джерелаThe forecasting of financial series is part of the decision-making process of monetary policies by central banks. Mendes, Ferreira and Mendes (2020) proposed a hybrid model that combines a VECM (Vector Error Correction Model) with a deep learning algorithm LSTM (Long Short-Term Memory) for a multivariate forecast of the U.S. stock index S&P500, using Nasdaq, Dow Jones and U.S. treasury bills for 3 months yields of the secondary market series, with weekly data, between 19/04/2019 and 17/04/2020. In this dissertation, this article was replicated, and a similar hybrid model was constructed with the same purpose and an 86% lower MAPE forecast error was obtained (4% versus 28%), even including the COVID-19 crisis. The time period without the crisis was analyzed and a MAPE of 1.9% was obtained. It was found that data leakage between the test and training periods is a problem that impairs the results. Different ways of constructing the hybrid model were compared by varying the number of lags and training epochs in LSTM, the impact of using the log-series was verified, and benchmarking with univariate and multivariate LSTM was made. In addition, granger causality was tested between the time periods with strong intervention by the FED (1970s and 1980s, and the COVID-19 crisis in February 2020) concluding that the changes in yields Granger cause the stock indices returns. In contrast, this causal relationship outside these time periods was the opposite, with the indices returns causing the changes in yields.
Rohovets, Taras. "Machine learning algorithms to predict stocks movements with Python language and dedicated libraries." Master's thesis, 2019. http://hdl.handle.net/10400.26/30163.
Повний текст джерелаRita, Nicole Oliveira. "Machine learning techniques for predicting the stock market using daily market variables." Master's thesis, 2020. http://hdl.handle.net/10362/94992.
Повний текст джерелаPredicting the stock market was never seen as an easy task. The complexity of the financial systems makes it extremely difficult for anything or anyone to predict what the future of prices holds, let it be a day, a week, a month or even a year. Many variables influence the market’s volatility and some of these may even be the gut feeling of an investor on a specific day. Several machine learning techniques were already applied to forecast multiple stock market indexes, some presenting good values of accuracy when it comes to predict whether the prices will go up or down, and low values of error when dealing with regression data. This work aims to apply some state-of-the-art algorithms and compare their performance with Long Short-term Memory (LSTM) as well as between each other. The variables used to this empirical work were the prices of the Dow Jones Industrial Average (DJIA) registered for every business day, from January 1st of 2006 to January 1st of 2018, for 29 companies. Some changes and adjustments were made to the original variables to present different data types to the algorithms. To ensure good quality and certainty when evaluating the flexibility and stability of each model, the error measure used was the Root Mean Squared Error and the Mann-Whitney U test was also applied to assess statistical significance of the results obtained.
Prever a bolsa nunca foi considerado ser uma tarefa fácil. A complexidade dos sistemas financeiros torna extremamente difícil que um ser humano ou uma máquina consigam prever o que o futuro dos preços reserva, seja para um dia, uma semana, um mês ou um ano. Muitas variáveis influenciam a volatilidade do mercado e algumas podem até ser a confiança de um investidor em apostar em determinada empresa, naquele dia específico. Várias técnicas de aprendizagem automática foram aplicadas ao longo do tempo para prever vários índices de bolsas, algumas apresentando bons valores de precisão quando se tratou de prever se os preços subiam ou desciam e outras, baixos valores de erro ao lidar com dados de regressão. Este trabalho tem como objetivo aplicar alguns dos mais conhecidos algoritmos e comparar os seus desempenhos com o Long Short-Term Memory (LSTM), e entre si. As variáveis utilizadas para a elaboração deste trabalho empírico foram os preços da Dow Jones Industrial Average (DJIA) registados para todos os dias úteis, de 1 de Janeiro de 2006 a 1 de Janeiro de 2018, para 29 empresas. Algumas alterações e ajustes foram efetuados sobre as variáveis originais de forma a construír diferentes tipos de dados para posteriormente dar aos algoritmos. Para garantir boa qualidade e veracidade ao avaliar a flexibilidade e estabilidade de cada modelo, a medida de erro utilizada foi o erro médio quadrático da raíz e, de seguida, o teste U de Mann-Whitney foi aplicado para avaliar a significância estatística dos resultados obtidos.