点击以下链接可以一键运行例程，无需安装配置环境 ↓ ↓ ↓
Pycaret 3.0 功能抢先体验
https://www.heywhale.com/mw/project/62d378a2de3942457a52c905
Pycaret简介
PyCaret 是一个开源、低代码的Python机器学习库，仅用几行代码替换数百行代码，可自动执行机器学习工作流，是一种端到端的机器学习和模型管理工具。
使用手册
官方的使用手册：Welcome to PyCaret - PyCaret Official
支持功能
Pycaret 模块支持的功能有数据处理，模型训练，参数搜索，模型可解释性，模型选择，实验日志查询。
Cheat Sheet
安装Pycaret 3.0 预览版
!pip install llvmlite==0.38.1 -i https://pypi.tuna.tsinghua.edu.cn/simple --ignore-installed
!pip install pycaret==3.0.0rc3 -i https://pypi.tuna.tsinghua.edu.cn/simple #--ignore-installed查看安装结果
import pycaret
print(pycaret.__version__)> 3.0.0.rc3
Pycaret 内置数据集
from pycaret.datasets import get_data
all_datasets = get_data('index')
all_datasets| Dataset | Data Types | Default Task | Target Variable 1 | Target Variable 2 | # Instances | # Attributes | Missing Values | |
|---|---|---|---|---|---|---|---|---|
| 0 | anomaly | Multivariate | Anomaly Detection | None | None | 1000 | 10 | N |
| 1 | france | Multivariate | Association Rule Mining | InvoiceNo | Description | 8557 | 8 | N |
| 2 | germany | Multivariate | Association Rule Mining | InvoiceNo | Description | 9495 | 8 | N |
| 3 | bank | Multivariate | Classification (Binary) | deposit | None | 45211 | 17 | N |
| 4 | blood | Multivariate | Classification (Binary) | Class | None | 748 | 5 | N |
| 5 | cancer | Multivariate | Classification (Binary) | Class | None | 683 | 10 | N |
| 6 | credit | Multivariate | Classification (Binary) | default | None | 24000 | 24 | N |
| 7 | diabetes | Multivariate | Classification (Binary) | Class variable | None | 768 | 9 | N |
| 8 | electrical_grid | Multivariate | Classification (Binary) | stabf | None | 10000 | 14 | N |
| 9 | employee | Multivariate | Classification (Binary) | left | None | 14999 | 10 | N |
| 10 | heart | Multivariate | Classification (Binary) | DEATH | None | 200 | 16 | N |
| 11 | heart_disease | Multivariate | Classification (Binary) | Disease | None | 270 | 14 | N |
| 12 | hepatitis | Multivariate | Classification (Binary) | Class | None | 154 | 32 | Y |
| 13 | income | Multivariate | Classification (Binary) | income >50K | None | 32561 | 14 | Y |
| 14 | juice | Multivariate | Classification (Binary) | Purchase | None | 1070 | 15 | N |
| 15 | nba | Multivariate | Classification (Binary) | TARGET_5Yrs | None | 1340 | 21 | N |
| 16 | wine | Multivariate | Classification (Binary) | type | None | 6498 | 13 | N |
| 17 | telescope | Multivariate | Classification (Binary) | Class | None | 19020 | 11 | N |
| 18 | titanic | Multivariate | Classification (Binary) | Survived | None | 891 | 11 | Y |
| 19 | us_presidential_election_results | Multivariate | Classification (Binary) | party_winner | None | 497 | 7 | N |
| 20 | glass | Multivariate | Classification (Multiclass) | Type | None | 214 | 10 | N |
| 21 | iris | Multivariate | Classification (Multiclass) | species | None | 150 | 5 | N |
| 22 | poker | Multivariate | Classification (Multiclass) | CLASS | None | 100000 | 11 | N |
| 23 | questions | Multivariate | Classification (Multiclass) | Next_Question | None | 499 | 4 | N |
| 24 | satellite | Multivariate | Classification (Multiclass) | Class | None | 6435 | 37 | N |
| 25 | CTG | Multivariate | Classification (Multiclass) | NSP | None | 2129 | 40 | Y |
| 26 | asia_gdp | Multivariate | Clustering | None | None | 40 | 11 | N |
| 27 | elections | Multivariate | Clustering | None | None | 3195 | 54 | Y |
| 28 | Multivariate | Clustering | None | None | 7050 | 12 | N | |
| 29 | ipl | Multivariate | Clustering | None | None | 153 | 25 | N |
| 30 | jewellery | Multivariate | Clustering | None | None | 505 | 4 | N |
| 31 | mice | Multivariate | Clustering | None | None | 1080 | 82 | Y |
| 32 | migration | Multivariate | Clustering | None | None | 233 | 12 | N |
| 33 | perfume | Multivariate | Clustering | None | None | 20 | 29 | N |
| 34 | pokemon | Multivariate | Clustering | None | None | 800 | 13 | Y |
| 35 | population | Multivariate | Clustering | None | None | 255 | 56 | Y |
| 36 | public_health | Multivariate | Clustering | None | None | 224 | 21 | N |
| 37 | seeds | Multivariate | Clustering | None | None | 210 | 7 | N |
| 38 | wholesale | Multivariate | Clustering | None | None | 440 | 8 | N |
| 39 | tweets | Text | NLP | tweet | None | 8594 | 2 | N |
| 40 | amazon | Text | NLP / Classification | reviewText | None | 20000 | 2 | N |
| 41 | kiva | Text | NLP / Classification | en | None | 6818 | 7 | N |
| 42 | spx | Text | NLP / Regression | text | None | 874 | 4 | N |
| 43 | wikipedia | Text | NLP / Classification | Text | None | 500 | 3 | N |
| 44 | automobile | Multivariate | Regression | price | None | 202 | 26 | Y |
| 45 | bike | Multivariate | Regression | cnt | None | 17379 | 15 | N |
| 46 | boston | Multivariate | Regression | medv | None | 506 | 14 | N |
| 47 | concrete | Multivariate | Regression | strength | None | 1030 | 9 | N |
| 48 | diamond | Multivariate | Regression | Price | None | 6000 | 8 | N |
| 49 | energy | Multivariate | Regression | Heating Load | Cooling Load | 768 | 10 | N |
| 50 | forest | Multivariate | Regression | area | None | 517 | 13 | N |
| 51 | gold | Multivariate | Regression | Gold_T+22 | None | 2558 | 121 | N |
| 52 | house | Multivariate | Regression | SalePrice | None | 1461 | 81 | Y |
| 53 | insurance | Multivariate | Regression | charges | None | 1338 | 7 | N |
| 54 | parkinsons | Multivariate | Regression | PPE | None | 5875 | 22 | N |
| 55 | traffic | Multivariate | Regression | traffic_volume | None | 48204 | 8 | N |
>
| Dataset | Data Types | Default Task | Target Variable 1 | Target Variable 2 | # Instances | # Attributes | Missing Values | |
|---|---|---|---|---|---|---|---|---|
| 0 | anomaly | Multivariate | Anomaly Detection | None | None | 1000 | 10 | N |
| 1 | france | Multivariate | Association Rule Mining | InvoiceNo | Description | 8557 | 8 | N |
| 2 | germany | Multivariate | Association Rule Mining | InvoiceNo | Description | 9495 | 8 | N |
| 3 | bank | Multivariate | Classification (Binary) | deposit | None | 45211 | 17 | N |
| 4 | blood | Multivariate | Classification (Binary) | Class | None | 748 | 5 | N |
| 5 | cancer | Multivariate | Classification (Binary) | Class | None | 683 | 10 | N |
| 6 | credit | Multivariate | Classification (Binary) | default | None | 24000 | 24 | N |
| 7 | diabetes | Multivariate | Classification (Binary) | Class variable | None | 768 | 9 | N |
| 8 | electrical_grid | Multivariate | Classification (Binary) | stabf | None | 10000 | 14 | N |
| 9 | employee | Multivariate | Classification (Binary) | left | None | 14999 | 10 | N |
| 10 | heart | Multivariate | Classification (Binary) | DEATH | None | 200 | 16 | N |
| 11 | heart_disease | Multivariate | Classification (Binary) | Disease | None | 270 | 14 | N |
| 12 | hepatitis | Multivariate | Classification (Binary) | Class | None | 154 | 32 | Y |
| 13 | income | Multivariate | Classification (Binary) | income >50K | None | 32561 | 14 | Y |
| 14 | juice | Multivariate | Classification (Binary) | Purchase | None | 1070 | 15 | N |
| 15 | nba | Multivariate | Classification (Binary) | TARGET_5Yrs | None | 1340 | 21 | N |
| 16 | wine | Multivariate | Classification (Binary) | type | None | 6498 | 13 | N |
| 17 | telescope | Multivariate | Classification (Binary) | Class | None | 19020 | 11 | N |
| 18 | titanic | Multivariate | Classification (Binary) | Survived | None | 891 | 11 | Y |
| 19 | us_presidential_election_results | Multivariate | Classification (Binary) | party_winner | None | 497 | 7 | N |
| 20 | glass | Multivariate | Classification (Multiclass) | Type | None | 214 | 10 | N |
| 21 | iris | Multivariate | Classification (Multiclass) | species | None | 150 | 5 | N |
| 22 | poker | Multivariate | Classification (Multiclass) | CLASS | None | 100000 | 11 | N |
| 23 | questions | Multivariate | Classification (Multiclass) | Next_Question | None | 499 | 4 | N |
| 24 | satellite | Multivariate | Classification (Multiclass) | Class | None | 6435 | 37 | N |
| 25 | CTG | Multivariate | Classification (Multiclass) | NSP | None | 2129 | 40 | Y |
| 26 | asia_gdp | Multivariate | Clustering | None | None | 40 | 11 | N |
| 27 | elections | Multivariate | Clustering | None | None | 3195 | 54 | Y |
| 28 | Multivariate | Clustering | None | None | 7050 | 12 | N | |
| 29 | ipl | Multivariate | Clustering | None | None | 153 | 25 | N |
| 30 | jewellery | Multivariate | Clustering | None | None | 505 | 4 | N |
| 31 | mice | Multivariate | Clustering | None | None | 1080 | 82 | Y |
| 32 | migration | Multivariate | Clustering | None | None | 233 | 12 | N |
| 33 | perfume | Multivariate | Clustering | None | None | 20 | 29 | N |
| 34 | pokemon | Multivariate | Clustering | None | None | 800 | 13 | Y |
| 35 | population | Multivariate | Clustering | None | None | 255 | 56 | Y |
| 36 | public_health | Multivariate | Clustering | None | None | 224 | 21 | N |
| 37 | seeds | Multivariate | Clustering | None | None | 210 | 7 | N |
| 38 | wholesale | Multivariate | Clustering | None | None | 440 | 8 | N |
| 39 | tweets | Text | NLP | tweet | None | 8594 | 2 | N |
| 40 | amazon | Text | NLP / Classification | reviewText | None | 20000 | 2 | N |
| 41 | kiva | Text | NLP / Classification | en | None | 6818 | 7 | N |
| 42 | spx | Text | NLP / Regression | text | None | 874 | 4 | N |
| 43 | wikipedia | Text | NLP / Classification | Text | None | 500 | 3 | N |
| 44 | automobile | Multivariate | Regression | price | None | 202 | 26 | Y |
| 45 | bike | Multivariate | Regression | cnt | None | 17379 | 15 | N |
| 46 | boston | Multivariate | Regression | medv | None | 506 | 14 | N |
| 47 | concrete | Multivariate | Regression | strength | None | 1030 | 9 | N |
| 48 | diamond | Multivariate | Regression | Price | None | 6000 | 8 | N |
| 49 | energy | Multivariate | Regression | Heating Load | Cooling Load | 768 | 10 | N |
| 50 | forest | Multivariate | Regression | area | None | 517 | 13 | N |
| 51 | gold | Multivariate | Regression | Gold_T+22 | None | 2558 | 121 | N |
| 52 | house | Multivariate | Regression | SalePrice | None | 1461 | 81 | Y |
| 53 | insurance | Multivariate | Regression | charges | None | 1338 | 7 | N |
| 54 | parkinsons | Multivariate | Regression | PPE | None | 5875 | 22 | N |
| 55 | traffic | Multivariate | Regression | traffic_volume | None | 48204 | 8 | N |
Pycaret 时间序列预测
导入模块
# 导入Pycaret内置数据
from pycaret.datasets import get_data
# 导入Pycaret时间序列预测模型（3.x版本新增）
from pycaret.time_series import *
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')读取数据
# 使用内置数据airline
data = get_data('airline')
data>
Period 1949-01 112.0 1949-02 118.0 1949-03 132.0 1949-04 129.0 1949-05 121.0 Freq: M, Name: Number of airline passengers, dtype: float64
>
Period
1949-01 112.0
1949-02 118.0
1949-03 132.0
1949-04 129.0
1949-05 121.0
...
1960-08 606.0
1960-09 508.0
1960-10 461.0
1960-11 390.0
1960-12 432.0
Freq: M, Name: Number of airline passengers, Length: 144, dtype: float64
数据探索分析
# 时间序列绘制
data.plot()>
# 统计测试
check_stats()>
| Test | Test Name | Data | Property | Setting | Value | |
|---|---|---|---|---|---|---|
| 0 | Summary | Statistics | Transformed | Length | 144.0 | |
| 1 | Summary | Statistics | Transformed | # Missing Values | 0.0 | |
| 2 | Summary | Statistics | Transformed | Mean | 280.298611 | |
| 3 | Summary | Statistics | Transformed | Median | 265.5 | |
| 4 | Summary | Statistics | Transformed | Standard Deviation | 119.966317 | |
| 5 | Summary | Statistics | Transformed | Variance | 14391.917201 | |
| 6 | Summary | Statistics | Transformed | Kurtosis | -0.364942 | |
| 7 | Summary | Statistics | Transformed | Skewness | 0.58316 | |
| 8 | Summary | Statistics | Transformed | # Distinct Values | 118.0 | |
| 9 | White Noise | Ljung-Box | Transformed | Test Statictic | {'alpha': 0.05, 'K': 24} | 1606.083817 |
| 10 | White Noise | Ljung-Box | Transformed | Test Statictic | {'alpha': 0.05, 'K': 48} | 1933.155822 |
| 11 | White Noise | Ljung-Box | Transformed | p-value | {'alpha': 0.05, 'K': 24} | 0.0 |
| 12 | White Noise | Ljung-Box | Transformed | p-value | {'alpha': 0.05, 'K': 48} | 0.0 |
| 13 | White Noise | Ljung-Box | Transformed | White Noise | {'alpha': 0.05, 'K': 24} | False |
| 14 | White Noise | Ljung-Box | Transformed | White Noise | {'alpha': 0.05, 'K': 48} | False |
| 15 | Stationarity | ADF | Transformed | Stationarity | {'alpha': 0.05} | False |
| 16 | Stationarity | ADF | Transformed | p-value | {'alpha': 0.05} | 0.99188 |
| 17 | Stationarity | ADF | Transformed | Test Statistic | {'alpha': 0.05} | 0.815369 |
| 18 | Stationarity | ADF | Transformed | Critical Value 1% | {'alpha': 0.05} | -3.481682 |
| 19 | Stationarity | ADF | Transformed | Critical Value 5% | {'alpha': 0.05} | -2.884042 |
| 20 | Stationarity | ADF | Transformed | Critical Value 10% | {'alpha': 0.05} | -2.57877 |
| 21 | Stationarity | KPSS | Transformed | Trend Stationarity | {'alpha': 0.05} | True |
| 22 | Stationarity | KPSS | Transformed | p-value | {'alpha': 0.05} | 0.1 |
| 23 | Stationarity | KPSS | Transformed | Test Statistic | {'alpha': 0.05} | 0.09615 |
| 24 | Stationarity | KPSS | Transformed | Critical Value 10% | {'alpha': 0.05} | 0.119 |
| 25 | Stationarity | KPSS | Transformed | Critical Value 5% | {'alpha': 0.05} | 0.146 |
| 26 | Stationarity | KPSS | Transformed | Critical Value 2.5% | {'alpha': 0.05} | 0.176 |
| 27 | Stationarity | KPSS | Transformed | Critical Value 1% | {'alpha': 0.05} | 0.216 |
| 28 | Normality | Shapiro | Transformed | Normality | {'alpha': 0.05} | False |
| 29 | Normality | Shapiro | Transformed | p-value | {'alpha': 0.05} | 0.000068 |
寻找最佳模型
# 初始化
s = setup(data, fh = 12, session_id = 123)
# 模型比较
best = compare_models()>
| Description | Value | |
|---|---|---|
| 0 | session_id | 123 |
| 1 | Target | Number of airline passengers |
| 2 | Approach | Univariate |
| 3 | Exogenous Variables | Not Present |
| 4 | Original data shape | (144, 1) |
| 5 | Transformed data shape | (144, 1) |
| 6 | Transformed train set shape | (132, 1) |
| 7 | Transformed test set shape | (12, 1) |
| 8 | Rows with missing values | 0.0% |
| 9 | Fold Generator | ExpandingWindowSplitter |
| 10 | Fold Number | 3 |
| 11 | Enforce Prediction Interval | False |
| 12 | Seasonal Period(s) Tested | 12 |
| 13 | Seasonality Present | True |
| 14 | Seasonalities Detected | [12] |
| 15 | Primary Seasonality | 12 |
| 16 | Target Strictly Positive | True |
| 17 | Target White Noise | No |
| 18 | Recommended d | 1 |
| 19 | Recommended Seasonal D | 1 |
| 20 | Preprocess | False |
| 21 | CPU Jobs | -1 |
| 22 | Use GPU | False |
| 23 | Log Experiment | False |
| 24 | Experiment Name | ts-default-name |
| 25 | USI | 98d2 |
| Model | MASE | RMSSE | MAE | RMSE | MAPE | SMAPE | R2 | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|---|
| exp_smooth | Exponential Smoothing | 0.5716 | 0.5997 | 16.7767 | 19.7954 | 0.0422 | 0.0427 | 0.8954 | 0.0400 |
| ets | ETS | 0.5931 | 0.6212 | 17.4172 | 20.5108 | 0.0440 | 0.0445 | 0.8882 | 0.0767 |
| et_cds_dt | Extra Trees w/ Cond. Deseasonalize & Detrending | 0.6602 | 0.7288 | 19.4653 | 24.1050 | 0.0484 | 0.0484 | 0.8459 | 0.1167 |
| huber_cds_dt | Huber w/ Cond. Deseasonalize & Detrending | 0.6813 | 0.7866 | 20.0334 | 25.9670 | 0.0491 | 0.0499 | 0.8113 | 0.0267 |
| arima | ARIMA | 0.6830 | 0.6735 | 20.0069 | 22.2199 | 0.0501 | 0.0507 | 0.8677 | 0.0900 |
| lr_cds_dt | Linear w/ Cond. Deseasonalize & Detrending | 0.7004 | 0.7702 | 20.6084 | 25.4401 | 0.0509 | 0.0514 | 0.8215 | 0.0267 |
| ridge_cds_dt | Ridge w/ Cond. Deseasonalize & Detrending | 0.7004 | 0.7703 | 20.6086 | 25.4405 | 0.0509 | 0.0514 | 0.8215 | 0.0233 |
| lar_cds_dt | Least Angular Regressor w/ Cond. Deseasonalize & Detrending | 0.7004 | 0.7702 | 20.6084 | 25.4401 | 0.0509 | 0.0514 | 0.8215 | 0.0233 |
| en_cds_dt | Elastic Net w/ Cond. Deseasonalize & Detrending | 0.7029 | 0.7732 | 20.6816 | 25.5362 | 0.0511 | 0.0516 | 0.8201 | 0.0267 |
| lasso_cds_dt | Lasso w/ Cond. Deseasonalize & Detrending | 0.7048 | 0.7751 | 20.7373 | 25.6005 | 0.0512 | 0.0517 | 0.8193 | 0.0200 |
| catboost_cds_dt | CatBoost Regressor w/ Cond. Deseasonalize & Detrending | 0.7106 | 0.8146 | 20.9112 | 26.8907 | 0.0505 | 0.0509 | 0.8085 | 0.9433 |
| br_cds_dt | Bayesian Ridge w/ Cond. Deseasonalize & Detrending | 0.7112 | 0.7837 | 20.9213 | 25.8795 | 0.0515 | 0.0521 | 0.8144 | 0.0233 |
| knn_cds_dt | K Neighbors w/ Cond. Deseasonalize & Detrending | 0.7162 | 0.8157 | 21.1613 | 26.9700 | 0.0521 | 0.0529 | 0.7811 | 0.0300 |
| auto_arima | Auto ARIMA | 0.7181 | 0.7114 | 21.0297 | 23.4661 | 0.0525 | 0.0531 | 0.8509 | 1.6967 |
| gbr_cds_dt | Gradient Boosting w/ Cond. Deseasonalize & Detrending | 0.7938 | 0.9310 | 23.3723 | 30.7344 | 0.0569 | 0.0576 | 0.7417 | 0.0367 |
| xgboost_cds_dt | Extreme Gradient Boosting w/ Cond. Deseasonalize & Detrending | 0.8155 | 0.9591 | 24.0738 | 31.6950 | 0.0582 | 0.0592 | 0.7118 | 152.0800 |
| lightgbm_cds_dt | Light Gradient Boosting w/ Cond. Deseasonalize & Detrending | 0.8156 | 0.9117 | 24.0002 | 30.0956 | 0.0575 | 0.0587 | 0.7561 | 86.6467 |
| rf_cds_dt | Random Forest w/ Cond. Deseasonalize & Detrending | 0.8327 | 0.9465 | 24.5290 | 31.2635 | 0.0600 | 0.0606 | 0.7360 | 0.1400 |
| ada_cds_dt | AdaBoost w/ Cond. Deseasonalize & Detrending | 0.8825 | 1.0292 | 25.9471 | 33.9304 | 0.0619 | 0.0637 | 0.6725 | 0.0500 |
| llar_cds_dt | Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending | 0.9670 | 1.1915 | 28.4499 | 39.3303 | 0.0665 | 0.0693 | 0.5738 | 0.0233 |
| theta | Theta Forecaster | 0.9729 | 1.0306 | 28.3192 | 33.8639 | 0.0670 | 0.0700 | 0.6710 | 0.0167 |
| omp_cds_dt | Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending | 1.0090 | 1.2370 | 29.6294 | 40.8121 | 0.0685 | 0.0718 | 0.5462 | 0.0200 |
| dt_cds_dt | Decision Tree w/ Cond. Deseasonalize & Detrending | 1.0429 | 1.2226 | 30.4800 | 40.1912 | 0.0726 | 0.0753 | 0.5362 | 0.0333 |
| snaive | Seasonal Naive Forecaster | 1.1479 | 1.0945 | 33.3611 | 35.9139 | 0.0832 | 0.0879 | 0.6072 | 0.0133 |
| par_cds_dt | Passive Aggressive w/ Cond. Deseasonalize & Detrending | 1.2472 | 1.3081 | 36.7727 | 43.3215 | 0.0935 | 0.0961 | 0.4968 | 0.0233 |
| polytrend | Polynomial Trend Forecaster | 1.6523 | 1.9202 | 48.6301 | 63.4299 | 0.1170 | 0.1216 | -0.0784 | 0.0100 |
| croston | Croston | 1.9311 | 2.3517 | 56.6180 | 77.5856 | 0.1295 | 0.1439 | -0.6281 | 0.0100 |
| naive | Naive Forecaster | 2.3599 | 2.7612 | 69.0278 | 91.0322 | 0.1569 | 0.1792 | -1.2216 | 0.6467 |
| grand_means | Grand Means Forecaster | 5.5306 | 5.2596 | 162.4117 | 173.6492 | 0.4000 | 0.5075 | -7.0462 | 0.5100 |
Processing: 0%| | 0/125 [00:00<?, ?it/s]
# 查看最优模型
best>
ExponentialSmoothing
ExponentialSmoothing(seasonal='mul', sp=12, trend='add')
exp_smooth = create_model('exp_smooth')
print(exp_smooth)>
| cutoff | MASE | RMSSE | MAE | RMSE | MAPE | SMAPE | R2 | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1956-12 | 0.4985 | 0.5735 | 14.5584 | 18.7730 | 0.0366 | 0.0376 | 0.8853 |
| 1 | 1957-12 | 0.5088 | 0.5368 | 15.5548 | 18.2243 | 0.0420 | 0.0411 | 0.9130 |
| 2 | 1958-12 | 0.7075 | 0.6888 | 20.2167 | 22.3888 | 0.0479 | 0.0494 | 0.8879 |
| Mean | NaT | 0.5716 | 0.5997 | 16.7767 | 19.7954 | 0.0422 | 0.0427 | 0.8954 |
| SD | NaT | 0.0962 | 0.0647 | 2.4663 | 1.8475 | 0.0046 | 0.0049 | 0.0125 |
Processing: 0%| | 0/4 [00:00<?, ?it/s]
ExponentialSmoothing(seasonal='mul', sp=12, trend='add')
# 模型预测评估
pred = predict_model(exp_smooth)>
| Model | MASE | RMSSE | MAE | RMSE | MAPE | SMAPE | R2 | |
|---|---|---|---|---|---|---|---|---|
| 0 | Exponential Smoothing | 0.3383 | 0.4576 | 10.3023 | 15.8096 | 0.0221 | 0.0216 | 0.9549 |