Hybrid Conferencee

International Conference on Time Series Forecasting for Engineering Systems (ICTSFES - 26)

3rd - 4th July 2026 | Madrid, Spain
Sample Abstract
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Conference Brochure
Sample Full Paper
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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Madrid. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Madrid event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Madrid conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Madrid, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Madrid."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICTSFES aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Time series forecasting techniques in engineering
02
Machine learning for predictive analytics
03
Statistical methods for time series analysis
04
Real-time forecasting in engineering systems
05
Challenges in time series forecasting
06
Applications of forecasting in industry
07
Deep learning for time series prediction
08
Case studies in engineering forecasting
09
Impact of IoT on time series analysis
10
Data preprocessing for time series data
11
Visualization of forecasting results
12
Machine learning algorithms for time series
13
Future trends in forecasting technologies
14
Integration of diverse data sources
15
Ethical considerations in forecasting models
16
Adaptive forecasting methods using AI
17
Collaborative forecasting approaches
18
Benchmarking forecasting models
19
User experience in forecasting applications
20
Machine learning frameworks for time series