TimesFM（Time Series Foundation Model）时间序列预测的数据研究(3)
前一篇完成了 TimesFM 的运行
这篇针对里面运行的数据进行分析
根据代码 数据是来自 exp, exp 是来自引用 的 .utils.py
这是 utilis.py 的代码
是从Nixtla来的 尼克斯塔与时间序列预测
"""Forked from https://github.com/Nixtla/nixtla/blob/main/experiments/amazon-chronos/src/utils.py."""
# 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")
以 toursim 月预测来分析
相关数据可以查看我上传的资源
TimesFM 预测数据来源 TimesFM（时间序列基础模型）是由谷歌研究开发的一种预训练时间序列基础模型
https://download.csdn.net/download/chenchihwen/90124776?spm=1001.2014.3001.5503这里已经将最原始的的 json 转换成 excel 格式
我们来看training 原始的 [data.json.gz] 内容如下：
通过 tourism_monthly 的 metadata.json
{"freq": "M", "target": null, "feat_static_cat": [{"name": "feat_static_cat_0", "cardinality": "366"}], "feat_static_real": [], "feat_dynamic_real": [], "feat_dynamic_cat": [], "prediction_length": 24}
代码转译后 excel 如下，
T1 标签最后的 training 资料 是1992/7/31
我们来看下预测的结果，与实际数据的比较，还行
| unique_id | ds | y | unique_id | ds | timesfm | ||
| T1 | 1992/8/31 | 6611.1 | T1 | 1992/8/31 | 5975.3 | ||
| T1 | 1992/9/30 | 4150.2 | T1 | 1992/9/30 | 4250.7 | ||
| T1 | 1992/10/31 | 2841.0 | T1 | 1992/10/31 | 2843.6 | ||
| T1 | 1992/11/30 | 1813.4 | T1 | 1992/11/30 | 2144.1 | ||
| T1 | 1992/12/31 | 2261.1 | T1 | 1992/12/31 | 2206.6 | ||
| T1 | 1993/1/31 | 1873.6 | T1 | 1993/1/31 | 1862.6 | ||
| T1 | 1993/2/28 | 1772.8 | T1 | 1993/2/28 | 1961.5 | ||
| T1 | 1993/3/31 | 2049.6 | T1 | 1993/3/31 | 2007.9 | ||
| T1 | 1993/4/30 | 2932.3 | T1 | 1993/4/30 | 2531.2 | ||
| T1 | 1993/5/31 | 3113.3 | T1 | 1993/5/31 | 2908.7 | ||
| T1 | 1993/6/30 | 3461.5 | T1 | 1993/6/30 | 3474.2 | ||
| T1 | 1993/7/31 | 6265.7 | T1 | 1993/7/31 | 5924.7 | ||
| T1 | 1993/8/31 | 6857.8 | T1 | 1993/8/31 | 6098.7 | ||
| T1 | 1993/9/30 | 4346.1 | T1 | 1993/9/30 | 4134.4 | ||
| T1 | 1993/10/31 | 3154.7 | T1 | 1993/10/31 | 2737.1 | ||
| T1 | 1993/11/30 | 2142.2 | T1 | 1993/11/30 | 1909.0 | ||
| T1 | 1993/12/31 | 2375.7 | T1 | 1993/12/31 | 2115.6 | ||
| T1 | 1994/1/31 | 1981.1 | T1 | 1994/1/31 | 1823.7 | ||
| T1 | 1994/2/28 | 1959.9 | T1 | 1994/2/28 | 1850.2 | ||
| T1 | 1994/3/31 | 2466.3 | T1 | 1994/3/31 | 1935.1 | ||
| T1 | 1994/4/30 | 2851.7 | T1 | 1994/4/30 | 2440.0 | ||
| T1 | 1994/5/31 | 3671.8 | T1 | 1994/5/31 | 2753.3 | ||
| T1 | 1994/6/30 | 3806.8 | T1 | 1994/6/30 | 3497.1 | ||
| T1 | 1994/7/31 | 6995.0 | T1 | 1994/7/31 | 5773.2 |
实际跑出的预计数是长得这个样子， 会有 timesfm-q-0.1 : timesfm-q-0.9
是因为代码中设置了
在Python代码中，尤其是涉及到时间序列分析、预测等相关场景（从你之前提供的看起来和时间序列相关的代码链接推测），`quantiles` 通常有以下一些常见用途：
### 概率预测与不确定性量化方面
1. **表示预测区间**：在很多预测任务中，模型给出的不只是一个单一的预测值（比如预测某个时间点的数值），而是一个预测区间来体现不确定性。`quantiles` 可以用来定义这些区间的边界，例如，常见的会使用 0.05、0.5、0.95 等不同分位数对应的数值来表示预测区间的下限、中位数、上限。像如果预测某商品未来销量，通过计算不同 `quantiles` 的值，能知道销量大概率（比如 90% 的置信区间，对应 0.05 和 0.95 分位数）会落在哪个范围，以及最有可能的中间值（0.5 分位数即中位数）是多少。
2. **评估预测的不确定性**：在评估预测模型好坏时，除了看预测值与实际值的偏差（比如均方误差等指标衡量准确性），还需要考量预测的不确定性是否合理。通过比较模型输出的 `quantiles` 对应的预测区间和实际观测值落在该区间的比例等情况，可以判断模型对不确定性的把握能力。例如，如果模型声称某个 `quantiles` 对应的区间有 90% 的概率包含真实值，但实际多次验证下来真实值只有 50% 的情况落在该区间，那就说明模型对不确定性的估计可能不太准确。
timesfm forecast 是取中位数 也就是 timesfm-q-0.5
原文地址：https://blog.csdn.net/chenchihwen/article/details/144444583
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