미분류
ensemble of LSTM - Google 검색
https://www.google.com/search?q=ensemble+of+LSTM+&biw=1707&bih=867&sxsrf=APq-WBuUQoCQ2VxA1vsksu5Z4EWIESAhzg%3A1650209185658&ei=oTFcYv_tJ9CkoAT3nqyAAg&ved=0ahUKEwi_wsOitJv3AhVQEogKHXcPCyA4PBDh1QMIDg&uact=5&oq=ensemble+of+LSTM+&gs_lcp=Cgdnd3Mtd2l6EAMyBAgjECcyBAgAEB46BwgjELADECc6BwgAEEcQsANKBAhBGABKBAhGGABQ3B1Y3B1g6h9oA3ABeACAAYoBiAGKAZIBAzAuMZgBAKABAcgBB8ABAQ&sclient=gws-wizv1_covered.pdf
https://assets.researchsquare.com/files/rs-526234/v1_covered.pdf?c=1631869541LSTM based Ensemble Network to enhance the learning of long-term dependencies in chatbot | EDP Open
https://www.edp-open.org/articles/smdo/full_html/2020/01/smdo190024/smdo190024.html:: Journal of Korean Society of Industrial and Systems Engineering ::
http://www.ksie.ne.kr/journal/article.php?code=75877A Study on the Korean Interest Rate Spread Prediction Model Using the US Interest Rate Spread : SVR-Ensemble (RNN, LSTM, GRU) Model based -KOREASCHOLAR
https://db.koreascholar.com/article?code=400107Ensemble deep RNN, LSTM and GRU model. | Download Scientific Diagram
https://www.researchgate.net/figure/Ensemble-deep-RNN-LSTM-and-GRU-model_fig2_339456466Bagging & Boosting - 데이터 사이언스 사용 설명서
https://dsbook.tistory.com/325캐글로 보는 각 분석 분야별 우선적용 방법 확인 – Go Lab
http://machinelearningkorea.com/2019/05/21/%EC%BA%90%EA%B8%80%EB%A1%9C-%EB%B3%B4%EB%8A%94-%EA%B0%81-%EB%B6%84%EC%84%9D-%EB%B6%84%EC%95%BC%EB%B3%84-%EC%9A%B0%EC%84%A0%EC%A0%81%EC%9A%A9-%EB%B0%A9%EB%B2%95-%EC%93%B1-%EC%82%B4%ED%8E%B4%EB%B3%B4/딥러닝 텐서플로 교과서: 7.7 RNN, LSTM, GRU 성능 비교
https://thebook.io/080263/ch07/07/[딥러닝] LSTM, GRU 등 간단한 딥러닝을 이용한 주식 종가 예측 - DACON
https://dacon.io/codeshare/4527딥러닝 앙상블 모델로 가상화폐 시계열 예측하기
https://velog.io/@raqoon886/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EC%95%99%EC%83%81%EB%B8%94-%EB%AA%A8%EB%8D%B8%EB%A1%9C-%EA%B0%80%EC%83%81%ED%99%94%ED%8F%90-%EC%8B%9C%EA%B3%84%EC%97%B4-%EC%98%88%EC%B8%A1%ED%95%98%EA%B8%B0[Python] 성능 측정 지표 :: MAE, MSE, RMSE, MAPE, MPE, MSLE
https://mizykk.tistory.com/102텐서플로우(Tensorflow)란?
https://computer-science-student.tistory.com/73[RNN 개념정리] RNN/LSTM 기본개념
https://warm-uk.tistory.com/54[Deep Learning 시리즈] Backpropagation, 역전파 알아보기
https://evan-moon.github.io/2018/07/19/deep-learning-backpropagationIID 샘플
https://pasus.tistory.com/40IID
http://www.ktword.co.kr/test/view/view.php?m_temp1=5021기초통계학 - iid(independent and identically distribution)란무엇인가?
https://happyday5328.tistory.com/11[금융 머신러닝] 금융 데이터의 종류와 특성(1) | Q's Tech blog
https://karl6885.github.io/data_science/finance/2020/11/07/%EA%B8%88%EC%9C%B5-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-2-%EA%B8%88%EC%9C%B5-%EB%8D%B0%EC%9D%B4%ED%84%B0%EC%9D%98-%EC%A2%85%EB%A5%98%EC%99%80-%ED%8A%B9%EC%84%B1(1)/[ 딥러닝 ] 시퀀스 데이터 ( Sequence Data ) , RNN 순환 신경망 구조 : 네이버 블로그
https://blog.naver.com/PostView.naver?blogId=dbswldud15&logNo=221981717919&redirect=Dlog&widgetTypeCall=true&directAccess=falseLSTM & GRU
https://sooftware.io/lstm_gru/딥러닝 (7) - RNN(Recurrent Neural Network), LSTM, GRU
https://davinci-ai.tistory.com/30Tensorflow&Keras - LSTM 개념 및 사용법 정리
https://simpling.tistory.com/19Long Short-Term Memory (LSTM)
https://yjjo.tistory.com/17XGBoost를 이용한 주식 예측 - (3)
https://superhky.tistory.com/100[Data Analysis] 개요 / 데이터의 분류 및 특성
https://cinema4dr12.tistory.com/516Pandas 활용한 데이터 분석 입문(2) - DataFrame 데이터살펴보기, 수치형과 범주형 데이터
https://velog.io/@supremo7/Pandas-%ED%99%9C%EC%9A%A9%ED%95%9C-%EB%8D%B0%EC%9D%B4%ED%84%B0-%EB%B6%84%EC%84%9D-%EC%9E%85%EB%AC%B82시계열 분석 이론의 기초 :: Y.LAB
https://yamalab.tistory.com/112시계열 예측: ARIMA 대 LSTM 대 PROPHET
https://ichi.pro/ko/sigyeyeol-yecheug-arima-dae-lstm-dae-prophet-107907230218811[성능비교] Time Series Forecasting - ARIMA, FP, LSTM, Transformer, Informer - AI Study Note
https://doheon.github.io/%EC%84%B1%EB%8A%A5%EB%B9%84%EA%B5%90/time-series/ci-6.compare-post/[데이터 분석] ARIMA 모델을 활용한 CMA 잔고 분석
https://investraveler.tistory.com/103파이썬을 활용한 시계열 데이터 분석 A-Z 올인원 패키지 Online. | 패스트캠퍼스
https://fastcampus.co.kr/data_online_pyt[Python] 삼성전자 주가 예측 입니다 - DACON
https://dacon.io/codeshare/2570시계열 분석 시리즈 (4): Python auto_arima로 삼성 주가 제대로 예측하기 | Be Geeky
https://assaeunji.github.io/data%20analysis/2021-09-25-arimastock/[Python] 날씨 시계열 데이터(Kaggle)로 ARIMA 적용하기
https://leedakyeong.tistory.com/entry/Python-%EB%82%A0%EC%94%A8-%EC%8B%9C%EA%B3%84%EC%97%B4-%EB%8D%B0%EC%9D%B4%ED%84%B0Kaggle%EB%A1%9C-ARIMA-%EC%A0%81%EC%9A%A9%ED%95%98%EA%B8%B0GRU.pdf
http://contents2.kocw.or.kr/KOCW/data/document/2020/edu1/bdu/hongseungwook1118/132.pdfGRU 기반의 삼성전자 주가 예측 : 네이버 블로그
https://blog.naver.com/PostView.naver?blogId=beyondlegend&logNo=222515160840&parentCategoryNo=&categoryNo=93&viewDate=&isShowPopularPosts=true&from=search[LSTM/GRU] 주식가격 예측 모델 구현하기
https://data-analysis-expertise.tistory.com/67실시간 내 주가를 알려주는 Python Code
https://creativeworks.tistory.com/entry/%EC%8B%A4%EC%8B%9C%EA%B0%84-%EB%82%B4-%EC%A3%BC%EA%B0%80%EB%A5%BC-%EC%95%8C%EB%A0%A4%EC%A3%BC%EB%8A%94-Python-Code[Python] 파이썬 웹 크롤링 실시간 주가 정보 가져오기
https://yjshin.tistory.com/entry/Python-%ED%8C%8C%EC%9D%B4%EC%8D%AC-%EC%9B%B9-%ED%81%AC%EB%A1%A4%EB%A7%81-%EC%8B%A4%EC%8B%9C%EA%B0%84-%EC%A3%BC%EA%B0%80-%EC%A0%95%EB%B3%B4-%EA%B0%80%EC%A0%B8%EC%98%A4%EA%B8%B0Django(장고) 프레임워크로 주식 검색 웹 만들기 | Udemy
https://www.udemy.com/course/django-s/[Python/Django] 파이썬 주식 정보(뉴스) 페이지 만들기 - 5. 네이버 금융 크롤링 2
https://kante-kante.tistory.com/15[시계열 분석] 1. 시계열 데이터와 정상 과정(Stationary Process)
https://zephyrus1111.tistory.com/97Time Series 1: Stationary(정상성) vs. Non-Stationary(비정상성)
https://ktcf.tistory.com/87time series의 stationarity를 체크해봅시다. : frhyme.code
https://frhyme.github.io/python-lib/check_stationary-in-time-series/Augmented Dickey-Fuller Test - Stationary 확인
https://hongl.tistory.com/98(칼럼) 딥러닝 초보들이 흔히하는 실수 : 주식가격 예측 AI - 코딩애플 온라인 강좌
https://codingapple.com/unit/deep-learning-stock-price-ai/LSTM을 활용한 주식가격 예측
https://dschloe.github.io/python/python_edu/07_deeplearning/deep_learning_lstm/LSTM을 활용한 주가 예측
https://direction-f.tistory.com/23RNN Tutorial Part 3 - BPTT와 Vanishing Gradient 문제
http://aikorea.org/blog/rnn-tutorial-3/