TimesFM (Time Series Foundation Model) for timeseries forecasting
TimesFM (Time Series Foundation Model) is a pretrained timeseries foundation model developed by Google Research for timeseries forecasting. Paper: A decoderonly foundation model for timeseries forecasting, to appear in ICML 2024. Google Research blog Hugging Face checkpoint repo This repo contains the code to load public TimesFM checkpoints and run model inference. Please visit our
TimesFM (Time Series Foundation Model) is a pretrained timeseries foundation model developed by Google
Research for timeseries forecasting.
 Paper: A decoderonly foundation model for timeseries forecasting, to appear in ICML 2024.
 Google Research blog
 Hugging Face checkpoint repo
This repo contains the code to load public TimesFM checkpoints and run model
inference. Please visit our
Hugging Face checkpoint repo
to download model checkpoints.
This is not an officially supported Google product.
timesfm1.0200m is the first open model checkpoint:
 It performs univariate time series forecasting for context lengths up tp 512 timepoints and any horizon lengths, with an optional frequency indicator.
 It focuses on point forecasts, and does not support probabilistic forecasts. We experimentally offer quantile heads but they have not been calibrated after pretraining.
 It requires the context to be contiguous (i.e. no “holes”), and the context and the horizon to be of the same frequency.
Please refer to our result tables on the extended benchmarks and the long horizon benchmarks.
Please look into the README files in the respective benchmark directories within experiments/
for instructions for running TimesFM on the respective benchmarks.
We have two environment files. For GPU installation (assuming CUDA 12 has been
setup), you can create a conda environment tfm_env
from the base folder
through:
conda env create file=environment.yml
For a CPU setup please use,
conda env create file=environment_cpu.yml
to create the environment instead.
Follow by
conda activate tfm_env
pip install e .
to install the package.
Then the base class can be loaded as,
input_patch_len=32, output_patch_len=128, num_layers=20, model_dims=1280,

The context_len here can be set as the max context length of the model. You can provide shorter series to the
tfm.forecast()
function and the model will handle it. Currently the model handles a max context length of 512, which can be increased in later releases. The input time series can have any context length. Padding / truncation will be handled by the inference code if needed. 
The horizon length can be set to anything. We recommend setting it to the largest horizon length you would need in the forecasting tasks for your application. We generally recommend horizon length <= context length but it is not a requirement in the function call.
We provide APIs to forecast from either array inputs or pandas
dataframe. Both forecast methods expect (1) the input time series contexts, (2) along with their frequencies. Please look at the documentation of the functions tfm.forecast()
and tfm.forecast_on_df()
for detailed instructions.
In particular regarding the frequency, TimesFM expects a categorical indicator valued in {0, 1, 2}:
 0 (default): high frequency, long horizon time series. We recommend to use this for time series up to daily granularity.
 1: medium frequency time series. We recommend to use this for weekly and monthly data.
 2: low frequency, short horizon time series. We recommend to use this for anything beyond monthly, e.g. quarterly or yearly.
This categorical value should be directly provided with the array inputs. For dataframe inputs, we convert the conventional letter coding of frequencies to our expected categories, that
 0: T, MIN, H, D, B, U
 1: W, M
 2: Q, Y
Notice you do NOT have to strictly follow our recommendation here. Although this is our setup during model training and we expect it to offer the best forecast result, you can also view the frequency input as a free parameter and modify it per your specific use case.
Examples:
Array inputs, with the frequencies set to low, medium and high respectively.
import numpy as np 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] point_forecast, experimental_quantile_forecast = tfm.forecast( forecast_input, freq=frequency_input, )
pandas
dataframe, with the frequency set to “M” monthly.
import pandas as pd # e.g. input_df is # unique_id ds y # 0 T1 19751231 697458.0 # 1 T1 19760131 1187650.0 # 2 T1 19760229 1069690.0 # 3 T1 19760331 1078430.0 # 4 T1 19760430 1059910.0 # ... ... ... ... # 8175 T99 19860131 602.0 # 8176 T99 19860228 684.0 # 8177 T99 19860331 818.0 # 8178 T99 19860430 836.0 # 8179 T99 19860531 878.0 forecast_df = tfm.forecast_on_df( inputs=input_df, freq="M", # monthly value_name="y", num_jobs=1, )