Pre-Enrollment

11th January 2027

Final Paper Submission

16th January 2027

Registration Deadline

26th January`2027

Conference Date

10th Feb - 11th Feb 2027

Call for Papers

The International Conference on Time Series Analysis and Machine Learning invites researchers, academicians, industry experts, and practitioners to submit original and high-quality research contributions. This conference serves as a global platform for presenting innovative ideas, exchanging knowledge, and fostering interdisciplinary collaboration.

Focusing on key research areas such as Machine Learning, the conference aims to bridge theoretical advancements with real-world applications.

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

01
Time Series Forecasting With Machine Learning
02
Anomaly Detection In Time Series Data
03
Applications Of Time Series Analysis
04
Feature Extraction Techniques For Time Series
05
Real-time Time Series Processing Methods
06
Seasonal Decomposition Of Time Series
07
Machine Learning For Financial Time Series
08
Time Series Data Visualization Techniques
09
Predictive Modeling For Time Series Data
10
Challenges In Time Series Forecasting
11
Time Series Classification Methods
12
Machine Learning For Sensor Time Series
13
Temporal Data Mining Techniques
14
Time Series Analysis In Healthcare
15
Machine Learning For Climate Data
16
Data Preprocessing For Time Series Analysis
17
Future Trends In Time Series Research
18
Machine Learning For Energy Time Series
19
Time Series Data Integration Methods
20
Collaborative Time Series Analysis
Conference Registration

To confirm your participation and secure your presentation slot, authors of accepted papers must complete the registration. Early registration is highly recommended to enjoy discounted fees.

Register Now to Confirm Participation
Publication & Submission

Present your findings to a global audience. All accepted papers are eligible for publication in ISER-affiliated journals and conference proceedings, providing you with maximum academic visibility.

Submit Your Paper for Review