TS-Reasoner-7B
TS-Reasoner couples a frozen Time Series Foundation Model (TimesFM) with a Qwen2.5-7B LLM so the language model can reason over raw numerical time series. The TSFM's latent representations are aligned with the LLM's textual input space through a two-stage recipe: alignment pretraining on synthetic time series–caption pairs, followed by instruction finetuning.
📄 Paper: TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning | 💻 Code: Yu-Fangxu/TS-Reasoner
Usage
Loading is self-contained — the model code automatically downloads and attaches the frozen TimesFM backbone (google/timesfm-1.0-200m-pytorch) on first use:
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ParadiseYu/TS-Reasoner-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
"ParadiseYu/TS-Reasoner-7B", trust_remote_code=True, torch_dtype=torch.float16
).to("cuda").eval()
Prompts contain one <ts><ts/> placeholder per series (preceded by a value-scaling prefix); the raw series are passed to generate via the timeseries= kwarg as a (num_series, length, 2) fp16 tensor of [scaled_value, mask] pairs. See demo.py in the code repository for a complete helper (load_ts_reasoner / ask) that handles the scaling, prompt assembly, and padding:
# git clone https://github.com/Yu-Fangxu/TS-Reasoner && cd TS-Reasoner
from demo import load_ts_reasoner, ask
tokenizer, model = load_ts_reasoner()
answer = ask(
tokenizer, model,
question="Time series 1: <ts><ts/>\nWhat is the overall trend?",
timeseries=[[0.1, 0.3, 0.2, 0.5, 0.8, 1.2, 1.1, 1.6]],
)
print(answer)
Citation
@article{yu2025tsreasoner,
title={TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning},
author={Yu, Fangxu and Zhao, Hongyu and Zhou, Tianyi},
journal={arXiv preprint arXiv:2510.03519},
year={2025}
}