2.0.0 initial

v1/experiments/extended_benchmarks/README.md

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+# 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
+
+We need to add the following packages for running these benchmarks. Follow the installation instructions till before `poetry lock`. Then,
+
+```
+poetry add git+https://github.com/awslabs/gluon-ts.git
+poetry lock
+poetry install --only <pax or pytorch>
+```
+
+To run the timesfm on the benchmark do:
+
+```
+poetry run python3 -m experiments.extended_benchmarks.run_timesfm --model_path=google/timesfm-1.0-200m(-pytorch) --backend="gpu"
+```
+
+
+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 for TimesFM-1.0
+
+![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. The TimesFM latency numbers are from the PAX version.
+
+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.

v1/experiments/extended_benchmarks/run_timegpt.py

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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.
+
+"""Evaluation script for timegpt."""
+
+import os
+import sys
+import time
+
+from absl import flags
+import numpy as np
+import pandas as pd
+
+from ..baselines.timegpt_pipeline import run_timegpt
+from .utils import ExperimentHandler
+
+
+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()

v1/experiments/extended_benchmarks/run_timesfm.py

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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.
+"""Evaluation script for timesfm."""
+
+import os
+import sys
+import time
+
+from absl import flags
+import numpy as np
+import pandas as pd
+import timesfm
+
+from .utils import ExperimentHandler
+
+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_v2 = {}
+
+context_dict_v1 = {
+    "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,
+}
+
+_MODEL_PATH = flags.DEFINE_string("model_path", "google/timesfm-2.0-500m-jax",
+                                  "Path to model")
+_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 = []
+  model_path = _MODEL_PATH.value
+  num_layers = 20
+  max_context_len = 512
+  use_positional_embedding = True
+  context_dict = context_dict_v1
+  if "2.0" in model_path:
+    num_layers = 50
+    use_positional_embedding = False
+    max_context_len = 2048
+    context_dict = context_dict_v2
+
+  tfm = timesfm.TimesFm(
+      hparams=timesfm.TimesFmHparams(
+          backend="gpu",
+          per_core_batch_size=32,
+          horizon_len=128,
+          num_layers=num_layers,
+          context_len=max_context_len,
+          use_positional_embedding=use_positional_embedding,
+      ),
+      checkpoint=timesfm.TimesFmCheckpoint(huggingface_repo_id=model_path),
+  )
+  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 = max_context_len
+
+    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,
+        normalize=True,
+    )
+    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__":
+  FLAGS = flags.FLAGS
+  FLAGS(sys.argv)
+  main()

v1/experiments/extended_benchmarks/utils.py

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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.
+
+"""Forked from https://github.com/Nixtla/nixtla/blob/main/experiments/amazon-chronos/src/utils.py."""
+
+from functools import partial
+from itertools import repeat
+import multiprocessing
+import os
+from pathlib import Path
+from typing import List
+
+from gluonts.dataset import Dataset
+from gluonts.dataset.repository.datasets import (
+    dataset_names as gluonts_datasets,
+    get_dataset,
+)
+from gluonts.time_feature.seasonality import get_seasonality
+import numpy as np
+import pandas as pd
+from utilsforecast.evaluation import evaluate
+from utilsforecast.losses import mae, mase, smape
+
+
+def parallel_transform(inp):
+  ts, last_n = inp[0], inp[1]
+  return ExperimentHandler._transform_gluonts_instance_to_df(ts, last_n=last_n)
+
+
+def quantile_loss(
+    df: pd.DataFrame,
+    models: list,
+    q: float = 0.5,
+    id_col: str = "unique_id",
+    target_col: str = "y",
+) -> pd.DataFrame:
+  delta_y = df[models].sub(df[target_col], axis=0)
+  res = (
+      np.maximum(q * delta_y, (q - 1) * delta_y)
+      .groupby(df[id_col], observed=True)
+      .mean()
+  )
+  res.index.name = id_col
+  res = res.reset_index()
+  return res
+
+
+class ExperimentHandler:
+
+  def __init__(
+      self,
+      dataset: str,
+      quantiles: List[float] = list(np.arange(1, 10) / 10.0),
+      results_dir: str = "./results",
+      models_dir: str = "./models",
+  ):
+    if dataset not in gluonts_datasets:
+      raise Exception(
+          f"dataset {dataset} not found in gluonts "
+          f"available datasets: {', '.join(gluonts_datasets)}"
+      )
+    self.dataset = dataset
+    self.quantiles = quantiles
+    self.level = self._transform_quantiles_to_levels(quantiles)
+    self.results_dir = results_dir
+    self.models_dir = models_dir
+    # defining datasets
+    self._maybe_download_m3_or_m5_file(self.dataset)
+    gluonts_dataset = get_dataset(self.dataset)
+    self.horizon = gluonts_dataset.metadata.prediction_length
+    if self.horizon is None:
+      raise Exception(
+          f"horizon not found for dataset {self.dataset} "
+          "experiment cannot be run"
+      )
+    self.freq = gluonts_dataset.metadata.freq
+    # get_seasonality() returns 1 for freq='D', override this to 7. This significantly improves the accuracy of
+    # statistical models on datasets like m5/nn5_daily. The models like AutoARIMA/AutoETS can still set
+    # seasonality=1 internally on datasets like weather by choosing non-seasonal models during model selection.
+    if self.freq == "D":
+      self.seasonality = 7
+    else:
+      self.seasonality = get_seasonality(self.freq)
+    self.gluonts_train_dataset = gluonts_dataset.train
+    self.gluonts_test_dataset = gluonts_dataset.test
+    self._create_dir_if_not_exists(self.results_dir)
+    try:
+      multiprocessing.set_start_method("spawn")
+    except RuntimeError:
+      print("Multiprocessing context has already been set.")
+
+  @staticmethod
+  def _maybe_download_m3_or_m5_file(dataset: str):
+    if dataset[:2] == "m3":
+      m3_file = Path.home() / ".gluonts" / "datasets" / "M3C.xls"
+      if not m3_file.exists():
+        from datasetsforecast.m3 import M3
+        from datasetsforecast.utils import download_file
+
+        download_file(m3_file.parent, M3.source_url)
+    elif dataset == "m5":
+      m5_raw_dir = Path.home() / ".gluonts" / "m5"
+      if not m5_raw_dir.exists():
+        import zipfile
+        from datasetsforecast.m5 import M5
+        from datasetsforecast.utils import download_file
+
+        download_file(m5_raw_dir, M5.source_url)
+        with zipfile.ZipFile(m5_raw_dir / "m5.zip", "r") as zip_ref:
+          zip_ref.extractall(m5_raw_dir)
+
+  @staticmethod
+  def _transform_quantiles_to_levels(quantiles: List[float]) -> List[int]:
+    level = [
+        int(100 - 200 * q) for q in quantiles if q < 0.5
+    ]  # in this case mean=mediain
+    level = sorted(list(set(level)))
+    return level
+
+  @staticmethod
+  def _create_dir_if_not_exists(directory: str):
+    Path(directory).mkdir(parents=True, exist_ok=True)
+
+  @staticmethod
+  def _transform_gluonts_instance_to_df(
+      ts: dict,
+      last_n: int | None = None,
+  ) -> pd.DataFrame:
+    start_period = ts["start"]
+    start_ds, freq = start_period.to_timestamp(), start_period.freq
+    target = ts["target"]
+    ds = pd.date_range(start=start_ds, freq=freq, periods=len(target))
+    if last_n is not None:
+      target = target[-last_n:]
+      ds = ds[-last_n:]
+    ts_df = pd.DataFrame({"unique_id": ts["item_id"], "ds": ds, "y": target})
+    return ts_df
+
+  @staticmethod
+  def _transform_gluonts_dataset_to_df(
+      gluonts_dataset: Dataset,
+      last_n: int | None = None,
+  ) -> pd.DataFrame:
+    with multiprocessing.Pool(os.cpu_count()) as pool:  # Create a process pool
+      results = pool.map(
+          parallel_transform, zip(gluonts_dataset, repeat(last_n))
+      )
+    df = pd.concat(results)
+    df = df.reset_index(drop=True)
+    return df
+
+  @property
+  def train_df(self) -> pd.DataFrame:
+    train_df = self._transform_gluonts_dataset_to_df(self.gluonts_train_dataset)
+    return train_df
+
+  @property
+  def test_df(self) -> pd.DataFrame:
+    test_df = self._transform_gluonts_dataset_to_df(
+        self.gluonts_test_dataset,
+        last_n=self.horizon,
+    )
+    # Make sure that only the first backtest window is used for evaluation on `traffic` / `exchange_rate` datasets
+    return test_df.groupby("unique_id", sort=False).head(self.horizon)
+
+  def save_dataframe(self, df: pd.DataFrame, file_name: str):
+    df.to_csv(f"{self.results_dir}/{file_name}", index=False)
+
+  def save_results(
+      self, fcst_df: pd.DataFrame, total_time: float, model_name: str
+  ):
+    self.save_dataframe(
+        fcst_df,
+        f"{model_name}-{self.dataset}-fcst.csv",
+    )
+    time_df = pd.DataFrame({"time": [total_time], "model": model_name})
+    self.save_dataframe(
+        time_df,
+        f"{model_name}-{self.dataset}-time.csv",
+    )
+
+  def fcst_from_level_to_quantiles(
+      self,
+      fcst_df: pd.DataFrame,
+      model_name: str,
+  ) -> pd.DataFrame:
+    fcst_df = fcst_df.copy()
+    cols = ["unique_id", "ds", model_name]
+    for q in self.quantiles:
+      if q == 0.5:
+        col = f"{model_name}"
+      else:
+        lv = int(100 - 200 * q)
+        hi_or_lo = "lo" if lv > 0 else "hi"
+        lv = abs(lv)
+        col = f"{model_name}-{hi_or_lo}-{lv}"
+      q_col = f"{model_name}-q-{q}"
+      fcst_df[q_col] = fcst_df[col].values
+      cols.append(q_col)
+    return fcst_df[cols]
+
+  def evaluate_models(self, models: List[str]) -> pd.DataFrame:
+    fcsts_df = []
+    times_df = []
+    for model in models:
+      fcst_method_df = pd.read_csv(
+          f"{self.results_dir}/{model}-{self.dataset}-fcst.csv"
+      ).set_index(["unique_id", "ds"])
+      fcsts_df.append(fcst_method_df)
+      time_method_df = pd.read_csv(
+          f"{self.results_dir}/{model}-{self.dataset}-time.csv"
+      )
+      times_df.append(time_method_df)
+    fcsts_df = pd.concat(fcsts_df, axis=1).reset_index()
+    fcsts_df["ds"] = pd.to_datetime(fcsts_df["ds"])
+    times_df = pd.concat(times_df)
+    return self.evaluate_from_predictions(
+        models=models, fcsts_df=fcsts_df, times_df=times_df
+    )
+
+  def evaluate_from_predictions(
+      self, models: List[str], fcsts_df: pd.DataFrame, times_df: pd.DataFrame
+  ) -> pd.DataFrame:
+    test_df = self.test_df
+    train_df = self.train_df
+    test_df = test_df.merge(fcsts_df, how="left")
+    assert test_df.isna().sum().sum() == 0, "merge contains nas"
+    # point evaluation
+    point_fcsts_cols = ["unique_id", "ds", "y"] + models
+    test_df["unique_id"] = test_df["unique_id"].astype(str)
+    train_df["unique_id"] = train_df["unique_id"].astype(str)
+    mase_seas = partial(mase, seasonality=self.seasonality)
+    eval_df = evaluate(
+        test_df[point_fcsts_cols],
+        train_df=train_df,
+        metrics=[smape, mase_seas, mae],
+    )
+    # probabilistic evaluation
+    eval_prob_df = []
+    for q in self.quantiles:
+      prob_cols = [f"{model}-q-{q}" for model in models]
+      eval_q_df = quantile_loss(test_df, models=prob_cols, q=q)
+      eval_q_df[prob_cols] = eval_q_df[prob_cols] * self.horizon
+      eval_q_df = eval_q_df.rename(columns=dict(zip(prob_cols, models)))
+      eval_q_df["metric"] = f"quantile-loss-{q}"
+      eval_prob_df.append(eval_q_df)
+    eval_prob_df = pd.concat(eval_prob_df)
+    eval_prob_df = eval_prob_df.groupby("metric").sum().reset_index()
+    total_y = test_df["y"].sum()
+    eval_prob_df[models] = eval_prob_df[models] / total_y
+    eval_prob_df["metric"] = "scaled_crps"
+    eval_df = pd.concat([eval_df, eval_prob_df]).reset_index(drop=True)
+    eval_df = eval_df.groupby("metric").mean(numeric_only=True).reset_index()
+    eval_df = eval_df.melt(
+        id_vars="metric", value_name="value", var_name="model"
+    )
+    times_df.insert(0, "metric", "time")
+    times_df = times_df.rename(columns={"time": "value"})
+    eval_df = pd.concat([eval_df, times_df])
+    eval_df.insert(0, "dataset", self.dataset)
+    eval_df = eval_df.sort_values(["dataset", "metric", "model"])
+    eval_df = eval_df.reset_index(drop=True)
+    return eval_df
+
+
+if __name__ == "__main__":
+  multiprocessing.set_start_method("spawn")