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{
"python.pythonPath": "/usr/bin/python3"
}
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---
license: apache-2.0
library_name: timesfm
pipeline_tag: time-series-forecasting
---
# 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](https://arxiv.org/abs/2310.10688), to appear in ICML 2024.
* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/)
* [GitHub repo](https://github.com/google-research/timesfm)
**Authors**: Google Research
This is not an officially supported Google product.
## Checkpoint timesfm-1.0-200m
`timesfm-1.0-200m` is the first open model checkpoint:
- It performs univariate time series forecasting for context lengths up to 512 time points 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.
## Benchmarks
Please refer to our result tables on the [extended benchmarks](https://github.com/google-research/timesfm/blob/master/experiments/extended_benchmarks/tfm_results.png) and the [long horizon benchmarks](https://github.com/google-research/timesfm/blob/master/experiments/long_horizon_benchmarks/tfm_long_horizon.png).
Please look into the README files in the respective benchmark directories within `experiments/` for instructions for running TimesFM on the respective benchmarks.
## Installation
This HuggingFace repo hosts TimesFm checkpoints. Please visit our [GitHub repo](https://github.com/google-research/timesfm) and follow the instructions there to install the `timesfm` library for model inference.
In particular, the dependency `lingvo` does not support ARM architectures and the inference code is not working for machines with Apple silicon. We are aware of this issue and are working on a solution. Stay tuned.
## Usage
### Initialize the model and load a checkpoint.
Then the base class can be loaded as,
```python
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
```python
input_patch_len=32,
output_patch_len=128,
num_layers=20,
model_dims=1280,
```
1. 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 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.
2. 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.
```python
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.
```python
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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__init__.py 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TimesFM init file."""
from __future__ import absolute_import
from .src.patched_decoder import PatchedTimeSeriesDecoder
from .src.timesfm import TimesFm
from .src.timesfm import freq_map
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# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#!/bin/bash
gdown --fuzzy https://drive.google.com/file/d/1alE33S1GmP5wACMXaLu50rDIoVzBM4ik/view?usp=share_link
unzip all_six_datasets.zip
mv all_six_datasets/* .
rm -rf all_six_datasets*
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# How to Contribute
We would love to accept your patches and contributions to this project.
## Before you begin
### Sign our Contributor License Agreement
Contributions to this project must be accompanied by a
[Contributor License Agreement](https://cla.developers.google.com/about) (CLA).
You (or your employer) retain the copyright to your contribution; this simply
gives us permission to use and redistribute your contributions as part of the
project.
If you or your current employer have already signed the Google CLA (even if it
was for a different project), you probably don't need to do it again.
Visit <https://cla.developers.google.com/> to see your current agreements or to
sign a new one.
### Review our Community Guidelines
This project follows [Google's Open Source Community
Guidelines](https://opensource.google/conduct/).
## Contribution process
### Code Reviews
All submissions, including submissions by project members, require review. We
use [GitHub pull requests](https://docs.github.com/articles/about-pull-requests)
for this purpose.
environment.yml 0 → 100644
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name: tfm_env
channels:
- conda-forge
- defaults
- anaconda
dependencies:
- jupyterlab
- pip
- python=3.10
- pip:
- huggingface_hub[cli]
- utilsforecast
- praxis
- paxml
- jax[cuda12]==0.4.26
- einshape
environment_cpu.yml 0 → 100644
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name: tfm_env
channels:
- conda-forge
- defaults
- anaconda
dependencies:
- jupyterlab
- pip
- python=3.10
- pip:
- huggingface_hub[cli]
- utilsforecast
- praxis
- paxml
- jax[cpu]==0.4.26
- einshape
experiments/baselines/__init__.py 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
\ No newline at end of file
experiments/baselines/timegpt_pipeline.py 0 → 100644
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple
import os
import pandas as pd
from gluonts.time_feature.seasonality import get_seasonality as _get_seasonality
from tqdm import tqdm
from utilsforecast.processing import (
backtest_splits,
drop_index_if_pandas,
join,
maybe_compute_sort_indices,
take_rows,
vertical_concat,
)
from time import time
from dotenv import load_dotenv
from nixtla import NixtlaClient
def get_seasonality(freq: str) -> int:
return _get_seasonality(freq, seasonalities={"D": 7})
def maybe_convert_col_to_datetime(df: pd.DataFrame, col_name: str) -> pd.DataFrame:
if not pd.api.types.is_datetime64_any_dtype(df[col_name]):
df = df.copy()
df[col_name] = pd.to_datetime(df[col_name])
return df
def zero_pad_time_series(df, freq, min_length=36):
"""If time_series length is less than min_length, front pad it with zeros."""
# 1. Calculate required padding for each unique_id
value_counts = df["unique_id"].value_counts()
to_pad = value_counts[value_counts < min_length].index
# 2. Create a new DataFrame to hold padded data
padded_data = []
for unique_id in to_pad:
# 2a. Filter data for the specific unique_id
subset = df[df["unique_id"] == unique_id]
if len(subset) > min_length:
padded_data.append(subset)
else:
# 2b. Determine earliest date and calculate padding dates
start_date = subset["ds"].min()
padding_dates = pd.date_range(
end=start_date,
periods=min_length - len(subset) + 1,
freq=freq, # 'MS' for month start
)[
:-1
] # Exclude the start_date itself
# 2c. Create padding data
padding_df = pd.DataFrame(
{"ds": padding_dates, "unique_id": unique_id, "y": 0} # Zero padding
)
# 2d. Combine original and padding data, and append to the list
padded_data.append(pd.concat([padding_df, subset]).sort_values("ds"))
# 3. Combine all padded data and original data (unchanged)
result_df = pd.concat(padded_data + [df[~df["unique_id"].isin(to_pad)]])
return result_df
class Forecaster:
"""Borrowed from
https://github.com/Nixtla/nixtla/tree/main/experiments/foundation-time-series-arena/xiuhmolpilli/models.
"""
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
) -> pd.DataFrame:
raise NotImplementedError
def cross_validation(
self,
df: pd.DataFrame,
h: int,
freq: str,
n_windows: int = 1,
step_size: int | None = None,
) -> pd.DataFrame:
df = maybe_convert_col_to_datetime(df, "ds")
# mlforecast cv code
results = []
sort_idxs = maybe_compute_sort_indices(df, "unique_id", "ds")
if sort_idxs is not None:
df = take_rows(df, sort_idxs)
splits = backtest_splits(
df,
n_windows=n_windows,
h=h,
id_col="unique_id",
time_col="ds",
freq=pd.tseries.frequencies.to_offset(freq),
step_size=h if step_size is None else step_size,
)
for _, (cutoffs, train, valid) in tqdm(enumerate(splits)):
if len(valid.columns) > 3:
raise NotImplementedError(
"Cross validation with exogenous variables is not yet supported."
)
y_pred = self.forecast(
df=train,
h=h,
freq=freq,
)
y_pred = join(y_pred, cutoffs, on="unique_id", how="left")
result = join(
valid[["unique_id", "ds", "y"]],
y_pred,
on=["unique_id", "ds"],
)
if result.shape[0] < valid.shape[0]:
raise ValueError(
"Cross validation result produced less results than expected. "
"Please verify that the frequency parameter (freq) matches your series' "
"and that there aren't any missing periods."
)
results.append(result)
out = vertical_concat(results)
out = drop_index_if_pandas(out)
first_out_cols = ["unique_id", "ds", "cutoff", "y"]
remaining_cols = [c for c in out.columns if c not in first_out_cols]
fcst_cv_df = out[first_out_cols + remaining_cols]
return fcst_cv_df
class TimeGPT(Forecaster):
"""Borrowed from
https://github.com/Nixtla/nixtla/tree/main/experiments/foundation-time-series-arena/xiuhmolpilli/models.
We modify the class to take care of edge cases.
"""
def __init__(
self,
api_key: str | None = None,
base_url: Optional[str] = None,
max_retries: int = 1,
model: str = "timegpt-1",
alias: str = "TimeGPT",
):
self.api_key = api_key
self.base_url = base_url
self.max_retries = max_retries
self.model = model
self.alias = alias
def _get_client(self) -> NixtlaClient:
if self.api_key is None:
api_key = os.environ["NIXTLA_API_KEY"]
else:
api_key = self.api_key
return NixtlaClient(
api_key=api_key,
base_url=self.base_url,
max_retries=self.max_retries,
)
def forecast(
self,
df: pd.DataFrame,
h: int,
freq: str,
level: List = [90.0],
chunk_size: Optional[int] = None,
) -> pd.DataFrame:
client = self._get_client()
fcst_df = None
if chunk_size is None:
fcst_df = client.forecast(
df=df,
h=h,
freq=freq,
level=level,
model=self.model,
)
else:
all_unique_ids = df["unique_id"].unique()
all_fcst_df = []
for i in range(0, len(all_unique_ids), chunk_size):
chunk_ids = all_unique_ids[i : i + chunk_size]
chunk_df = df[df["unique_id"].isin(chunk_ids)]
fct_chunk_df = client.forecast(
df=chunk_df,
h=h,
freq=freq,
level=level,
)
all_fcst_df.append(fct_chunk_df)
fcst_df = pd.concat(all_fcst_df)
fcst_df["ds"] = pd.to_datetime(fcst_df["ds"])
replace_dict = {}
for col in fcst_df.columns:
if col.startswith("TimeGPT"):
replace_dict[col] = col.replace("TimeGPT", self.alias)
fcst_df = fcst_df.rename(columns=replace_dict)
return fcst_df
def run_timegpt(
train_df: pd.DataFrame,
horizon: int,
freq: str,
seasonality: int,
level: List[int],
dataset: str,
model: str = "timegpt-1",
) -> Tuple[pd.DataFrame, float, str]:
os.environ["NIXTLA_ID_AS_COL"] = "true"
model = TimeGPT("nixtla-tok-uV1RLF3oVWJ8R2Bq1eqqiCxWRceUKVr751OpiM9AGdhQkRZfjgL0ceG1TYVE34wCXjdAbq41ZES0mIZy", model="timegpt-1", alias=model) # #TODO:添加密匙
padded_train_df = zero_pad_time_series(train_df, freq)
init_time = time()
# For these datasets the API fails if we do not chunk.
if dataset in ["m5", "m4_quarterly"]:
chunk_size = 5000
else:
chunk_size = None
fcsts_df = model.forecast(
df=padded_train_df, h=horizon, level=level, freq=freq, chunk_size=chunk_size
)
total_time = time() - init_time
# In case levels are not returned we replace the levels with the mean predictions.
# Note that this does not affect the results table as we only compare on point
# forecastign metrics.
for lvl in level:
if f"{model.alias}-lo-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-lo-{lvl}"] = fcsts_df[model.alias]
if f"{model.alias}-hi-{lvl}" not in fcsts_df.columns:
fcsts_df[f"{model.alias}-hi-{lvl}"] = fcsts_df[model.alias]
return fcsts_df, total_time, model.alias
experiments/environment.yml 0 → 100644
View file @ efb99f11
name: tfm_env
channels:
- conda-forge
- defaults
- anaconda
dependencies:
- jupyterlab
- pip
- python=3.10
- pip:
- datasetsforecast
- fire
- git+https://github.com/awslabs/gluon-ts.git
- huggingface_hub[cli]
- neuralforecast
- orjson
- statsforecast
- utilsforecast
- git+https://github.com/amazon-science/chronos-forecasting.git
- praxis
- paxml
- jax[cuda12]==0.4.26
- einshape
- python-dotenv
- nixtla>=0.5.1
- rich
experiments/environment_cpu.yml 0 → 100644
View file @ efb99f11
name: tfm_env
channels:
- conda-forge
- defaults
- anaconda
dependencies:
- jupyterlab
- pip
- python=3.10
- pip:
- datasetsforecast
- fire
- git+https://github.com/awslabs/gluon-ts.git
- huggingface_hub[cli]
- neuralforecast
- orjson
- statsforecast
- utilsforecast
- git+https://github.com/amazon-science/chronos-forecasting.git
- praxis
- paxml
- jax[cpu]==0.4.26
- einshape
- python-dotenv
- nixtla>=0.5.1
- rich
experiments/extended_benchmarks/README.md 0 → 100644
View file @ efb99f11
# Extended Benchmarks
The benchmark setting has been borrowed from Nixtla's original [benchmarking](https://github.com/AzulGarza/nixtla/tree/main/experiments/amazon-chronos) of time-series foundation models against a strong statistical ensemble. Later more datasets were added by the Chronos team in this [pull request](https://github.com/shchur/nixtla/tree/chronos-full-eval/experiments/amazon-chronos). We compare on all the datasets in this extended benchmarks.
## Running TimesFM on the benchmark
Install the environment and the package as detailed in the main README and then follow the steps from the base directory.
```
conda activate tfm_env
TF_CPP_MIN_LOG_LEVEL=2 XLA_PYTHON_CLIENT_PREALLOCATE=false python3 -m experiments.extended_benchmarks.run_timesfm --model_path=<model_path> --backend="gpu"
```
In the above, `<model_path>` should point to the checkpoint directory that can be downloaded from HuggingFace.
Note: In the current version of TimesFM we focus on point forecasts and therefore the mase, smape have been calculated using the quantile head corresponding to the median i.e 0.5 quantile. We do offer 10 quantile heads but they have not been calibrated after pretraining. We recommend using them with caution or calibrate/conformalize them on a hold out for your applications. More to follow on later versions.
## Benchmark Results
![Benchmark Results Table](./tfm_extended_new.png)
__Update:__ We have added TimeGPT-1 to the benchmark results. We had to remove the Dominick dataset as we were not able to run TimeGPT-1 on this benchmark. Note that the previous results including Dominick remain available at `./tfm_results.png`. In order to reproduce the results for TimeGPT-1, please run `run_timegpt.py`.
_Remark:_ All baselines except the ones involving TimeGPT were run performed on a [g2-standard-32](https://cloud.google.com/compute/docs/gpus). Since TimeGPT-1 can only be accessed by an API, the time column might not reflect the true speed of the model as it also includes the communication cost. Moreover, we are not sure about the exact backend hardware for TimeGPT.
We can see that TimesFM performs the best in terms of both mase and smape. More importantly it is much faster than the other methods, in particular it is more than 600x faster than StatisticalEnsemble and 80x faster than Chronos (Large).
Note: This benchmark only compares on `one` small horizon window for long horizon datasets like ETT hourly and 15 minutes. More in depth comparison on longer horizon rolling validation tasks are presented in our long horizon benchmarks.
\ No newline at end of file
experiments/extended_benchmarks/run_timegpt.py 0 → 100644
View file @ efb99f11
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation script for timegpt."""
import os
import sys
import time
from absl import flags
import numpy as np
import pandas as pd
from experiments.baselines.timegpt_pipeline import run_timegpt #TODO:路径
from utils import ExperimentHandler #TODO:路径
dataset_names = [
"m1_monthly",
"m1_quarterly",
"m1_yearly",
"m3_monthly",
"m3_other",
"m3_quarterly",
"m3_yearly",
"m4_quarterly",
"m4_yearly",
"tourism_monthly",
"tourism_quarterly",
"tourism_yearly",
"nn5_daily_without_missing",
"m5",
"nn5_weekly",
"traffic",
"weather",
"australian_electricity_demand",
"car_parts_without_missing",
"cif_2016",
"covid_deaths",
"ercot",
"ett_small_15min",
"ett_small_1h",
"exchange_rate",
"fred_md",
"hospital",
]
_MODEL_NAME = flags.DEFINE_string(
"model_name",
"timegpt-1-long-horizon",
"Path to model, can also be set to timegpt-1",
)
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")
QUANTILES = list(np.arange(1, 10) / 10.0)
def main():
results_list = []
run_id = np.random.randint(100000)
model_name = _MODEL_NAME.value
for dataset in dataset_names:
print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
exp = ExperimentHandler(dataset, quantiles=QUANTILES)
train_df = exp.train_df
horizon = exp.horizon
seasonality = exp.seasonality
freq = exp.freq
level = exp.level
fcsts_df, total_time, model_name = run_timegpt(
train_df=train_df,
horizon=exp.horizon,
model=model_name,
seasonality=seasonality,
freq=freq,
dataset=dataset,
level=level,
)
time_df = pd.DataFrame({"time": [total_time], "model": model_name})
fcsts_df = exp.fcst_from_level_to_quantiles(fcsts_df, model_name)
results = exp.evaluate_from_predictions(
models=[model_name], fcsts_df=fcsts_df, times_df=time_df
)
print(results, flush=True)
results_list.append(results)
results_full = pd.concat(results_list)
save_path = os.path.join(_SAVE_DIR.value, str(run_id))
print(f"Saving results to {save_path}", flush=True)
os.makedirs(save_path, exist_ok=True)
results_full.to_csv(f"{save_path}/results.csv")
if __name__ == "__main__":
FLAGS = flags.FLAGS
FLAGS(sys.argv)
main()
experiments/extended_benchmarks/run_timesfm.py 0 → 100644
View file @ efb99f11
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Evaluation script for timesfm."""
import os
import sys
import time
from absl import flags
import numpy as np
import pandas as pd
from paxml import checkpoints
sys.path.append(os.getcwd())
from src import timesfm #TODO:路径
from experiments.extended_benchmarks.utils import ExperimentHandler
from jax.lib import xla_bridge
# sugon测试
dataset_names = [ # context_len=512
"ett_small_15min",
# "traffic",
# "m3_quarterly",
# "m3_yearly",
"tourism_yearly",
]
# dataset_names = [
# "m1_monthly",
# "m1_quarterly",
# "m1_yearly",
# "m3_monthly",
# "m3_other",
# "m3_quarterly",
# "m3_yearly",
# "m4_quarterly",
# "m4_yearly",
# "tourism_monthly",
# "tourism_quarterly",
# "tourism_yearly",
# "nn5_daily_without_missing",
# "m5",
# "nn5_weekly",
# "traffic",
# "weather",
# "australian_electricity_demand",
# "car_parts_without_missing",
# "cif_2016",
# "covid_deaths",
# "ercot",
# "ett_small_15min",
# "ett_small_1h",
# "exchange_rate",
# "fred_md",
# "hospital",
# ]
context_dict = {
"cif_2016": 32,
"tourism_yearly": 64,
"covid_deaths": 64,
"tourism_quarterly": 64,
"tourism_monthly": 64,
"m1_monthly": 64,
"m1_quarterly": 64,
"m1_yearly": 64,
"m3_monthly": 64,
"m3_other": 64,
"m3_quarterly": 64,
"m3_yearly": 64,
"m4_quarterly": 64,
"m4_yearly": 64,
}
# TODO：模型位置
_MODEL_PATH = flags.DEFINE_string(
"model_path", "model/checkpoints", "Path to model"
)
#TODO： 参数调整
_BATCH_SIZE = flags.DEFINE_integer("batch_size", 64, "Batch size")
_HORIZON = flags.DEFINE_integer("horizon", 128, "Horizon")
_BACKEND = flags.DEFINE_string("backend", "gpu", "Backend")
_NUM_JOBS = flags.DEFINE_integer("num_jobs", 1, "Number of jobs")
_SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")
QUANTILES = list(np.arange(1, 10) / 10.0)
def main():
results_list = []
tfm = timesfm.TimesFm(
context_len=512,
horizon_len=_HORIZON.value,
input_patch_len=32,
output_patch_len=128,
num_layers=20,
model_dims=1280,
backend=_BACKEND.value,
per_core_batch_size=_BATCH_SIZE.value,
quantiles=QUANTILES,
)
tfm.load_from_checkpoint( # 检查点,加载模型
_MODEL_PATH.value,
checkpoint_type=checkpoints.CheckpointType.FLAX,
)
run_id = np.random.randint(100000)
model_name = "timesfm"
for dataset in dataset_names:
print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
exp = ExperimentHandler(dataset, quantiles=QUANTILES)
if dataset in context_dict:
context_len = context_dict[dataset]
else:
context_len = 512
train_df = exp.train_df
freq = exp.freq
init_time = time.time()
fcsts_df = tfm.forecast_on_df(
inputs=train_df,
freq=freq,
value_name="y",
model_name=model_name,
forecast_context_len=context_len,
num_jobs=_NUM_JOBS.value,
)
total_time = time.time() - init_time
time_df = pd.DataFrame({"time": [total_time], "model": model_name})
results = exp.evaluate_from_predictions(
models=[model_name], fcsts_df=fcsts_df, times_df=time_df
)
print(results, flush=True)
results_list.append(results)
results_full = pd.concat(results_list)
save_path = os.path.join(_SAVE_DIR.value, str(run_id))
print(f"Saving results to {save_path}", flush=True)
os.makedirs(save_path, exist_ok=True)
results_full.to_csv(f"{save_path}/results.csv")
if __name__ == "__main__":
# # debug1-测试torch-gpu\jax-gpu\TensorFlow-gpu
jax_test=xla_bridge.get_backend().platform
print(jax_test)
if not (jax_test=='gpu'):
exit()
FLAGS = flags.FLAGS
FLAGS(sys.argv)
main()
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