Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In thi
In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI) is an indicator that describes the amount of chlorophyll (the green mass) and shows the...
The commonly used methods in statistical forecasting mainly include time series and regression analysis. The time series method analyzes the input in chronological order, researches, and predicts the output. Wang et al. used ARIMA to determine the influence weights of the travel time of the first ...
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model. - Maxwell-Jia/TSFpaper
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model. - ddz16/TSFpaper
(Upper) Examples of snapshots of simulation ata30,b90, andc150 min after tsunami generation, and (lower) those of time series at selected DART stations near the epicenter. The results are based on src01 (Fig.4a) Full size image Fig. 7 ...
Gica, P. Y. Huang, H. O. Mofjeld, M. C. Spillane, V. V. Titov, and E. I. Tolkova (2015), Detiding DARTVR buoy data for real-time extraction of source coefficients for operational tsunami forecasting, Pure Appl. Geophys., in press....
Percival DB, Denbo DW, Ebl´e MC, Gica E, Huang PY, Mofjeld HO, Spillane MC, Titov VV, Tolkova EI (2015) Detiding DART R buoy data for real-time extraction of source coefficients for operational tsunami forecasting. Pure Appl Geophys 172(6):1653-1678. doi:10.1007/s00024-014-0962-...
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model. - slience-me/TSFpaper
24-12-19 NBEATS-KAN NIPSW 2024 Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks None 25-02-10 TimeKAN ICLR 2025 TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting TimeKAN 25-02-25 TSKANMixer AAAIW 2025 TSKANMixer: Kolmogorov...