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Topic · Signal Processing

Time Series Analysis and Forecasting

Time Series Analysis and Forecasting is a research topic within Signal Processing. Science Explorer counts 24k research works in it since 1950. 19.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the clustering, classification, and analysis of time series data. It covers various algorithms and techniques such as dynamic time warping, feature extraction, deep learning, symbolic representation, multivariate classification, similarity measures, dimensionality reduction, and pattern discovery.

  • Time Series
  • Clustering
  • Dynamic Time Warping
  • Feature Extraction
  • Deep Learning
  • Symbolic Representation
  • Multivariate Classification
  • Similarity Measures
  • Dimensionality Reduction
  • Pattern Discovery
Research works
24k
fractional, since 1950
In the world top 10%
4.8k
per year above
Top-10% rate
19.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+48%
the tick is no change

Which countries lead Time Series Analysis and Forecasting research?

By volume, China and the United States publish the most (2.4k and 865 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.4k works
  2. 2 United States 865 works
  3. 3 India 588 works
  4. 4 Germany 334 works
  5. 5 United Kingdom 216 works
  6. 6 Italy 197 works
  7. 7 France 190 works
  8. 8 Japan 190 works
  9. 9 South Korea 173 works
  10. 10 Spain 150 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 44.9%United States: 16.4%India: 11.2%Germany: 6.3%6 others listed: 21.2%45%largest
China2,367 · 44.9%United States865 · 16.4%India588 · 11.2%Germany334 · 6.3%6 others listed1,116 · 21.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Time Series Analysis and Forecasting research?

By volume in 2022–2025, Tsinghua University publishes the most Time Series Analysis and Forecasting research, followed by Harbin Institute of Technology and Beihang University.

By volume, 2022–2025

  1. 1 Tsinghua UniversityChina 42 works
  2. 2 Harbin Institute of TechnologyChina 39 works
  3. 3 Beihang UniversityChina 39 works
  4. 4 Zhejiang UniversityChina 34 works
  5. 5 Beijing Jiaotong UniversityChina 32 works
  6. 6 Chinese Academy of SciencesChina 31 works
  7. 7 National University of Defense TechnologyChina 31 works
  8. 8 University of Electronic Science and Technology of ChinaChina 30 works
  9. 9 Shanghai Jiao Tong UniversityChina 30 works
  10. 10 Beijing University of Posts and TelecommunicationsChina 28 works

Who are the leading researchers in Time Series Analysis and Forecasting?

The most-cited researchers publishing on Time Series Analysis and Forecasting include Yoshua Bengio and Philip S. Yu.

  1. 1 Yoshua Bengio Canada 17k citations
  2. 2 Philip S. Yu United States 6.6k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Time Series Analysis and Forecasting research done?

The largest centres of Time Series Analysis and Forecasting research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Hangzhou (China).

Largest cities, 2022–2025

  1. 1 Beijing China 501 works
  2. 2 Shanghai China 181 works
  3. 3 Nanjing China 123 works
  4. 4 Hangzhou China 110 works
  5. 5 Chengdu China 104 works
  6. 6 Xi'an China 103 works
  7. 7 Wuhan China 96 works
  8. 8 Guangzhou China 91 works
  9. 9 Seoul South Korea 85 works
  10. 10 Changsha China 78 works
See Time Series Analysis and Forecasting on the map

Where is the best place to study Time Series Analysis and Forecasting?

Among universities, judged by research, Shandong Institute of Business and Technology, Beijing Jiaotong University and University of Technology Sydney score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 29.69%fractional works in this node (log) →share in the world top 10% →Shandong Institute of Business and Technology: 12, 42.5%Beijing Jiaotong University: 32, 22.3%University of Technology Sydney: 14, 39.2%Aalborg University: 16, 43.7%University of Electronic Science and Technology of China: 30, 26.1%Beihang University: 38, 30.6%National University of Defense Technology: 31, 16.3%Beijing University of Posts and Telecommunications: 28, 16.7%Tsinghua University: 42, 27.8%Shandong University: 21, 31.7%Aalborg UniversityShandong Institute o…University of Techno…Beijing Jiaotong Uni…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Shandong Institute of Business and TechnologyChina 57.942.5%20.0×12 —
2 Beijing Jiaotong UniversityChina 57.122.3%7.5×32 +111.1%
3 University of Technology SydneyAustralia 56.039.2%4.3×14 +169.9%
4 Aalborg UniversityDenmark 53.943.7%4.5×16 +77.5%
5 University of Electronic Science and Technology of ChinaChina 53.326.1%3.9×30 +189.8%
6 Beihang UniversityChina 53.130.6%5.0×38 +54.8%
7 National University of Defense TechnologyChina 51.816.3%5.6×31 +197.5%
8 Beijing University of Posts and TelecommunicationsChina 51.116.7%6.5×28 +145.4%
9 Tsinghua UniversityChina 49.827.8%3.1×42 +75.9%
10 Shandong UniversityChina 48.931.7%2.5×21 +281.7%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Time Series Analysis and Forecasting research growing?

Output in 2018–2022 was 48% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Time Series Analysis and Forecasting.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.