TimesFM Time Series Forecasting (Google AI, Jupyter, and GPUs)

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Supercharge your time series forecasting with the TimesFM model from Google Research! In this video, we show you how to harness the power of GPUs and Vertex AI Workbench notebooks to run this cutting-edge model.
Learn how to:
- Set up a GPU-enabled Jupyter notebook on Google Cloud
- Install required dependencies like Hugging Face Hub, PyTorch, and Jax
- Configure the TimesFM model for your forecasting needs
- Prepare your time series data using Pandas
- Generate forecasts and interpret the results
Whether you're a beginner or a pro, this tutorial will level up your time series skills.
Demonstration Code and Diagram: github.com/nodematiclabs/time...
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0:00 Conceptual Overview
1:17 Vertex AI Workbench (Jupyter)
2:40 TimesFM Setup
5:04 Time Series and Model
7:08 Synthetic Data (Pandas and Numpy)
8:29 Forecasting/Prediction

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Опубликовано:

1 июл 2024

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Комментарии : 8

@fathimashaniya9805 День назад

Hi, Thank you very much. Your vedio was very beneficial.

@ziqili2120 12 дней назад

Hi, thanks for your great video! Do you know how to finetune this model on some private dataset? I found the tutorials using Paxml framework are very limited. I've been stuck in the finetuning for weeks.

@nodematic 23 часа назад

It doesn't look like this is very well supported right now, but the model architecture means this should be relatively easy once it's better documented/supported. We'll try to create a video on this when the fine-tuning experience is improved.

@fathimashaniya9805 День назад

Can I ask u another question? How do I fine tune this model

@nodematic День назад

Fine-tuning does not appear to be supported right now, but they may add support for that in the future, since the model architecture lends itself well to fine-tuning.

@katarzynakuryo197 21 день назад

Hi, do you know if it is possible to use this model for multivariate time series?

@nodematic 21 день назад

Yes, the model is multivariate. Just be sure to include everything in the forecast call "inputs".

@katarzynakuryo197 20 дней назад

@@nodematic I want to train my model on both ts at once. Do you know why it is not working? data = pd.DataFrame({ 'ds': train_df.index, 'ts1': train_df["ts1"], 'ts2': train_df["ts2"], 'unique_id': 'sensor_1' }) forecast_df=tfm.forecast_on_df( inputs=data, freq="D", value_name=["ts1", "ts2"] )