Citation Analysis

HMM-LSTM Fusion Model for Economic Forecasting
Guhan Sivakumar
https://arxiv.org/abs/2501.02002
21
Citation mentions
16
Cited references
10
Sections
5,609
Words (approx)

References by Citation Intensity

Ordered by composite index (descending). Higher values indicate more intensive citation.

# Reference Year Mentions Breadth Sec. Wtd Share Composite Main %
1 Athey, Susan and Imbens, Guido W. 2019 2 1 4.0 0.095 0.511 100%
2 Zheng, Yuanhang and Xu, Zeshui and Xiao, Anran 2023 2 1 4.0 0.095 0.511 100%
3 Zhang, Junhuan and Wen, Jiaqi and Yang, Zhen 2022 2 1 4.0 0.095 0.511 100%
4 Yang, Yucheng and Zheng, Zhong and E, Weinan 2020 2 1 4.0 0.095 0.511 100%
5 Sima Siami- 2018 2 1 4.0 0.095 0.511 100%
6 T.W. Rikken 2022 1 1 2.0 0.048 0.406 100%
7 St.$\:$Louis$\:$Fed 1991 1 1 2.0 0.048 0.406 100%
8 Yahoo$\:$Finance 2024 1 1 2.0 0.048 0.406 100%
9 Willem Thorbecke 2002 1 1 2.0 0.048 0.406 100%
10 Dickey, D. and Fuller, Wayne 1979 1 1 2.0 0.048 0.406 100%
11 Rabiner, L.R. 1989 1 1 2.0 0.048 0.406 100%
12 Dempster, A.P. and Laird, N.M. and Rubin, D.B. 1977 1 1 2.0 0.048 0.406 100%
13 Viterbi, A. 1967 1 1 2.0 0.048 0.406 100%
14 Divyanshu Thakur 2018 1 1 2.0 0.048 0.406 100%
15 Hochreiter, Sepp and Schmidhuber, Jürgen 1997 1 1 2.0 0.048 0.406 100%
16 Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi 2017 1 1 2.0 0.048 0.406 100%
Measures: Mentions = total in-text citations; Breadth = distinct sections; Sec. Wtd = section-weighted count (body ×2, lit review/appendix ×0.5); Share = mentions / total citations in paper; Composite = geometric mean of normalised count, breadth, and main-text ratio; Main % = fraction of mentions in main text (excl. appendix). (self) = self-citation.