About me

I’m a passionate algorithm engineer specializing in machine learning and data analysis. I am at present working in Alibaba Group Damo Academy. The Homepage of our lab is DAMO-DI-M.

Research Interests

My research focuses on developing robust and efficient machine learning models for real-world applications, with particular emphasis on:

• Time Series Forecasting: Long-term multivariate forecasting, frequency-enhanced models, decomposition methods
• Foundation Models: Pre-trained models for weather forecasting and climate prediction
• Transformer Architectures: Novel attention mechanisms and efficient transformers for sequential data
• Energy & Climate Systems: Solar power forecasting, electricity load prediction, weather modeling
• Self-supervised Learning: Strategic pre-training methods to reduce overfitting in data-limited domains
• Multimodal Learning: Fusion of heterogeneous modalities using vector quantization frameworks

Expertise

• Deep Learning for Time Series Analysis
• Frequency Domain Methods & Spectral Analysis
• Foundation Models & Transfer Learning
• Weather & Climate Forecasting
• Energy Systems Optimization
• Extreme Event Prediction

Education

• M.S., Tsinghua University, 2018-2021
• Exchange Programme, Centrale Marseille, 2016-2018
• B.S., Tsinghua University, 2014-2018

Publications

1. [KDD 2025] Utilizing Strategic Pre-training to Reduce Overfitting: Baguan – A Pre-trained Weather Forecasting Model. Peisong Niu*, Ziqing Ma*, Tian Zhou*, Weiqi Chen, Lefei Shen, Rong Jin, Liang Sun.
2. [KDD 2024] FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting. Ziqing Ma*, Wenwei Wang*, Tian Zhou*, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin.
3. [CIKM 2023] GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting. Yanjun Zhao*, Ziqing Ma*, Tian Zhou*, Mengni Ye, Liang Sun, Yi Qian.
4. [ICASSP 2023] SaDI: A Self-adaptive Decomposed Interpretable Framework for Electricity Load Forecasting under Extreme Events. Hengbo LIU*, Ziqing MA*, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun.
5. [Neurips 2022] FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting. Tian Zhou*, Ziqing Ma*, Xue wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin.
6. [ICML 2022] FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. Tian Zhou*, Ziqing Ma*, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin.
7. [IEEE GRSL 2020] Hybrid Attention Networks for Flow and Pressure Forecasting in Water Distribution Systems. Ziqing Ma, Shuming Liu, Guancheng Guo, Xipeng Yu.
8. [IAAI 2023] eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms. Zhaoyang Zhu, Weiqi Chen, Rui Xia, Tian Zhou, Peisong Niu, Bingqing Peng, Wenwei Wang, Hengbo Liu, Ziqing Ma, Qingsong Wen, Liang Sun.
9. [IJCAI 2023] Transformer for time series a Survey. Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun.
10. [Water Resource Management 2020] Novel leakage detection and localization method based online spectrum pair and cubic interpolation search. Guancheng Guo, Xipeng Yu, Shuming Liu, Xiyan Xu, Ziqing Ma, Xiaoting Wang, Yujun Huang, Kate Smith.

Working Papers

1. [arXiv 2026] Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting. Ziqing Ma, Kai Ying, Xinyue Gu, Tian Zhou, Tianyu Zhu, Haifan Zhang, Peisong Niu, Wang Zheng, Cong Bai, Liang Sun. Paper
2. [arXiv 2026] Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction. Peisong Niu, Haifan Zhang, Yang Zhao, Tian Zhou, Ziqing Ma, Wenqiang Shen, Junping Zhao, Huiling Yuan, Liang Sun. Paper

Interests

• Snowboarding
• Scuba Diving
• Freediving