Digital Library
Search: "[ keyword: Time Series ]" (13)
Daily Stock Price Prediction Using Fuzzy Model
Hee Soo Hwang The KIPS Transactions:PartB ,
Vol. 15, No. 6, pp. 603-608,
Dec.
2008
10.3745/KIPSTB.2008.15.6.603
10.3745/KIPSTB.2008.15.6.603
Efficient Similarity Search in Multi-attribute Time Series Databases
Sang Jun Lee The KIPS Transactions:PartD,
Vol. 14, No. 7, pp. 727-732,
Dec.
2007
10.3745/KIPSTD.2007.14.7.727
10.3745/KIPSTD.2007.14.7.727
Development of a Deep Learning-based Midterm PM2.5 Prediction Model Adapting to Trend Changes
Dong Jun Min, Hyerim Kim, Sangkyun Lee The Transactions of the Korea Information Processing Society,
Vol. 13, No. 6, pp. 251-259,
Jun.
2024
https://doi.org/10.3745/TKIPS.2024.13.6.251
Keywords: Particulate Matter, PM2.5, Time Series Forecast, Deep Learning, Ensemble
https://doi.org/10.3745/TKIPS.2024.13.6.251
Keywords: Particulate Matter, PM2.5, Time Series Forecast, Deep Learning, Ensemble
Temporal Fusion Transformers and Deep Learning Methodsfor Multi-Horizon Time Series Forecasting
InKyung Kim, DaeHee Kim, Jaekoo Lee KIPS Transactions on Software and Data Engineering,
Vol. 11, No. 2, pp. 81-86,
Feb.
2022
https://doi.org/10.3745/KTSDE.2022.11.2.81
Keywords: Time Series, Multi-variate Data Analysis, Multi-horizon Forecasting, Deep Learning, Neural Networks
https://doi.org/10.3745/KTSDE.2022.11.2.81
Keywords: Time Series, Multi-variate Data Analysis, Multi-horizon Forecasting, Deep Learning, Neural Networks
Style-Based Transformer for Time Series Forecasting
Dong-Keon Kim, Kwangsu Kim KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 12, pp. 579-586,
Dec.
2021
https://doi.org/10.3745/KTSDE.2021.10.12.579
Keywords: Time Series Forecasting, Transformer, Generative Decoder, Style Transfer
https://doi.org/10.3745/KTSDE.2021.10.12.579
Keywords: Time Series Forecasting, Transformer, Generative Decoder, Style Transfer
Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model
In-Gyu Lee, Mi-Hwa Song KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 10, pp. 391-398,
Oct.
2021
https://doi.org/10.3745/KTSDE.2021.10.10.391
Keywords: Leased Line, Traffic Modeling, time series analysis, Deep Learning, RNN, LSTM
https://doi.org/10.3745/KTSDE.2021.10.10.391
Keywords: Leased Line, Traffic Modeling, time series analysis, Deep Learning, RNN, LSTM
Optimization of Post - Processing for Subsequence Matching in Time - Series Databases
Sang Wook Kim The KIPS Transactions:PartD,
Vol. 9, No. 4, pp. 555-560,
Aug.
2002
10.3745/KIPSTD.2002.9.4.555
10.3745/KIPSTD.2002.9.4.555
Hourly Prediction of Particulate Matter (PM2.5) Concentration Using Time Series Data and Random Forest
Deukwoo Lee, Soowon Lee KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 4, pp. 129-136,
Apr.
2020
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
https://doi.org/10.3745/KTSDE.2020.9.4.129
Keywords: Particulate Matter, PM2.5, Time Series Data, Machine Learning, Random Forest
Time Series Perturbation Modeling Algorithm - Combination of Genetic Programming and Quantum Mechanical Perturbation Theory
Geum Yong Lee The KIPS Transactions:PartB ,
Vol. 9, No. 3, pp. 277-286,
Jun.
2002
10.3745/KIPSTB.2002.9.3.277
10.3745/KIPSTB.2002.9.3.277
Data Mining Time Series Data With Virtual Transaction
Ung Mo Kim, Min Soo Kim, Chul Hwan Kim The KIPS Transactions:PartD,
Vol. 9, No. 2, pp. 251-257,
Apr.
2002
10.3745/KIPSTD.2002.9.2.251
10.3745/KIPSTD.2002.9.2.251
Indexing
All publications of KTSDE is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.
KTSDE is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).
