TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

Resources and Technical Documentation:

Paper: A decoder-only foundation model for time-series forecasting, ICML 2024.
Google Research blog
GitHub repo

Authors: Google Research

This is not an officially supported Google product.

Checkpoint timesfm-2.0-500m

timesfm-2.0-500m is the second open model checkpoint:

It performs univariate time series forecasting for context lengths up to 2048 time points and any horizon lengths, with an optional frequency indicator. Note that it can go even beyond 2048 context even though it was trained with that as the maximum context.
It focuses on point forecasts. We experimentally offer 10 quantile heads but they have not been calibrated after pretraining.
It ideally requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency. In case there are nans we fill in the missing values with linear interpolation before calling the model.

Installation

This HuggingFace repo hosts TimesFm checkpoints. Please visit our GitHub repo and follow the instructions for the PAX version to install the timesfm library for model inference.

Usage

Initialize the model and load a checkpoint.

Then the base class can be loaded as,

import timesfm

# For PAX
tfm = timesfm.TimesFm(
      hparams=timesfm.TimesFmHparams(
          backend=<backend>,
          per_core_batch_size=32,
          horizon_len=128,
          input_patch_len=32,
          output_patch_len=128,
          num_layers=50,
          model_dims=1280,
          use_positional_embedding=False,
      ),
      checkpoint=timesfm.TimesFmCheckpoint(
          huggingface_repo_id="google/timesfm-2.0-500m-jax"),
  )

Note that the five parameters are fixed to load the 500m model

input_patch_len=32,
output_patch_len=128,
num_layers=50,
model_dims=1280,
use_positional_embedding=False,

The context_len here can be set as the max context length of the model. You can provide a shorter series to the tfm.forecast() function and the model will handle it. Currently, the model handles a max context length of 2048, 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.

Perform inference

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,
)

Pretraining Data

It is important to list all the data sources in order to enable fair benchmarking. The TimesFM 2.0 series contains the pretraining set of TimesFM 1.0 along with these additional datasets (a subset of the LOTSA pretraining data that many other models are pretrained on):

Dataset	Download Source
azure_vm_traces	LOTSA Pretrain
residential_load_power	LOTSA Pretrain
borg_cluster_data	LOTSA Pretrain
residential_pv_power	LOTSA Pretrain
q_traffic	LOTSA Pretrain
london_smart_meters_with_missing	LOTSA Pretrain
taxi_30min	LOTSA Pretrain
solar_power	LOTSA Pretrain
wind_power	LOTSA Pretrain
kdd2022	LOTSA Pretrain
largest	LOTSA Pretrain
era5	LOTSA Pretrain
buildings	LOTSA Pretrain
cmip6	LOTSA Pretrain
china_air_quality	LOTSA Pretrain
beijing_air_quality	LOTSA Pretrain
subseasonal	LOTSA Pretrain
kaggle_web_traffic_weekly	LOTSA Pretrain
cdc_fluview_who_nrevss	LOTSA Pretrain
godaddy	LOTSA Pretrain

google
/

timesfm-2.0-500m-jax