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transformers/docs/source/en/model_doc/timesfm.md

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+*This model was released on 2023-10-14 and added to Hugging Face Transformers on 2025-04-16.*
+
+# TimesFM
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model proposed in [A decoder-only foundation model for time-series forecasting](https://huggingface.co/papers/2310.10688) by Abhimanyu Das, Weihao Kong, Rajat Sen, and  Yichen Zhou. It is a decoder only model that uses non-overlapping patches of time-series data as input and outputs some output patch length prediction in an autoregressive fashion.
+
+The abstract from the paper is the following:
+
+*Motivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised forecasting models for each individual dataset. Our model is based on pretraining a patched-decoder style attention model on a large time-series corpus, and can work well across different forecasting history lengths, prediction lengths and temporal granularities.*
+
+This model was contributed by [kashif](https://huggingface.co/kashif).
+The original code can be found [here](https://github.com/google-research/timesfm).
+
+To use the model:
+
+```python
+import numpy as np
+import torch
+from transformers import TimesFmModelForPrediction
+
+
+model = TimesFmModelForPrediction.from_pretrained(
+    "google/timesfm-2.0-500m-pytorch",
+    dtype=torch.bfloat16,
+    attn_implementation="sdpa",
+    device_map="auto"
+)
+
+
+ # Create dummy inputs
+forecast_input = [
+    np.sin(np.linspace(0, 20, 100)),
+    np.sin(np.linspace(0, 20, 200)),
+    np.sin(np.linspace(0, 20, 400)),
+]
+frequency_input = [0, 1, 2]
+
+# Convert inputs to sequence of tensors
+forecast_input_tensor = [
+    torch.tensor(ts, dtype=torch.bfloat16).to(model.device)
+    for ts in forecast_input
+]
+frequency_input_tensor = torch.tensor(frequency_input, dtype=torch.long).to(model.device)
+
+# Get predictions from the pre-trained model
+with torch.no_grad():
+    outputs = model(past_values=forecast_input_tensor, freq=frequency_input_tensor, return_dict=True)
+    point_forecast_conv = outputs.mean_predictions.float().cpu().numpy()
+    quantile_forecast_conv = outputs.full_predictions.float().cpu().numpy()
+```
+
+## TimesFmConfig
+
+[[autodoc]] TimesFmConfig
+
+## TimesFmModel
+
+[[autodoc]] TimesFmModel
+    - forward
+
+## TimesFmModelForPrediction
+
+[[autodoc]] TimesFmModelForPrediction
+    - forward