TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
- Paper: A decoder-only foundation model for time-series 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.
timesfm-1.0-200m is the first open model checkpoint:
- It performs univariate time series forecasting for context lengths up to 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 recommend at least 16GB RAM to load TimesFM dependencies.
For calling TimesFM, We have two environment files. Inside timesfm, 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.
Note:
-
Running the provided benchmarks would require additional dependencies. Please use the environment files under
experimentsinstead. -
The dependency
lingvodoes not support ARM architectures, and the code is not working for machines with Apple silicon. We are aware of this issue and are working on a solution. Stay tuned.
Then the base class can be loaded as,
import timesfm
tfm = timesfm.TimesFm(
context_len=<context>,
horizon_len=<horizon>,
input_patch_len=32,
output_patch_len=128,
num_layers=20,
model_dims=1280,
backend=<backend>,
)
tfm.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")Note that the four parameters are fixed to load the 200m model
input_patch_len=32,
output_patch_len=128,
num_layers=20,
model_dims=1280,-
The
context_lenhere can be set as the max context length of the model. It needs to be a multiplier ofinput_patch_len, i.e. a multiplier of 32. You can provide a shorter series to thetfm.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.
-
backendis one of "cpu", "gpu" or "tpu", case sensitive.
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 using this for time series up to daily granularity.
- 1: medium frequency time series. We recommend using this for weekly and monthly data.
- 2: low frequency, short horizon time series. We recommend using 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 1975-12-31 697458.0
# 1 T1 1976-01-31 1187650.0
# 2 T1 1976-02-29 1069690.0
# 3 T1 1976-03-31 1078430.0
# 4 T1 1976-04-30 1059910.0
# ... ... ... ...
# 8175 T99 1986-01-31 602.0
# 8176 T99 1986-02-28 684.0
# 8177 T99 1986-03-31 818.0
# 8178 T99 1986-04-30 836.0
# 8179 T99 1986-05-31 878.0
forecast_df = tfm.forecast_on_df(
inputs=input_df,
freq="M", # monthly
value_name="y",
num_jobs=-1,
)```
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