Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time Warping and $$k^*$$-Nearest Neighbors

2018 ◽  
pp. 97-111 ◽  
Author(s):  
Kei Nakagawa ◽  
Mitsuyoshi Imamura ◽  
Kenichi Yoshida
Keyword(s):  
Stock Price ◽  
Dynamic Time Warping ◽  
Nearest Neighbors ◽  
Time Warping ◽  
K Nearest Neighbors ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Dynamic Time

scholarly journals Stock price prediction usingk-medoids clustering with indexing dynamic time warping

2019 ◽  
Vol 102 (2) ◽  
pp. 3-8 ◽  
Author(s):  
Kei Nakagawa ◽  
Mitsuyoshi Imamura ◽  
Kenichi Yoshida
Keyword(s):  
Stock Price ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Dynamic Time

scholarly journals Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Network Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3879 ◽  
Author(s):  
Giovanni Saggio ◽  
Pietro Cavallo ◽  
Mariachiara Ricci ◽  
Vito Errico ◽  
Jonathan Zea ◽  
...  
Keyword(s):  
Sign Language ◽  
Dynamic Time Warping ◽  
Parametric Method ◽  
Nearest Neighbors ◽  
Wearable Electronics ◽  
Language Recognition ◽  
Time Warping ◽  
Sign Language Recognition ◽  
K Nearest Neighbors ◽  
Dynamic Time

We propose a sign language recognition system based on wearable electronics and two different classification algorithms. The wearable electronics were made of a sensory glove and inertial measurement units to gather fingers, wrist, and arm/forearm movements. The classifiers were k-Nearest Neighbors with Dynamic Time Warping (that is a non-parametric method) and Convolutional Neural Networks (that is a parametric method). Ten sign-words were considered from the Italian Sign Language: cose, grazie, maestra, together with words with international meaning such as google, internet, jogging, pizza, television, twitter, and ciao. The signs were repeated one-hundred times each by seven people, five male and two females, aged 29–54 y ± 10.34 (SD). The adopted classifiers performed with an accuracy of 96.6% ± 3.4 (SD) for the k-Nearest Neighbors plus the Dynamic Time Warping and of 98.0% ± 2.0 (SD) for the Convolutional Neural Networks. Our system was made of wearable electronics among the most complete ones, and the classifiers top performed in comparison with other relevant works reported in the literature.

scholarly journals LQ45 Stock Index Prediction using k-Nearest Neighbors Regression

2019 ◽  
Vol 8 (3) ◽  
pp. 2388-2391 ◽  
Keyword(s):  
Capital Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Numerical Data ◽  
Nearest Neighbors ◽  
Stock Index ◽  
K Nearest Neighbors ◽  
Stock Price Prediction ◽  
The Capital Market

The capital market is an organized financial system consisting of commercial banks, intermediary institutions in the financial sector and all outstanding securities. One of the benefits of the capital market is creating opportunities for the community to participate in economic activities, especially in investing. In daily stock trading activities, stock prices tend to have fluctuated. Therefore, stock price prediction is needed to help investors make decisions when they want to buy or sell their shares. One asset for investment is shares. One of the stock price indices that attracts many investors is the LQ45 stock index on the Indonesian stock exchange. One of the algorithms that can be used to predict is the k-Nearest Neighbors (kNN) algorithm. In the previous study, kNN had a higher accuracy than the moving average method of 14.7%. This study uses kNN regression method because it predicts numerical data. The results of the research in making the LQ45 stock index prediction application have been successfully built. The highest accuracy achieved reaches 91.81% by WSKT share.

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