更新的StatsForecast库显示错误“预测”未在Python中定义

7rfyedvj  于 2022-12-05  发布在  Python
关注(0)|答案(1)|浏览(180)

我试图在python中复制这段用于统计预测的代码，我遇到了一个奇怪的错误“name 'forecasts' is not defined”，这很奇怪，因为我之前能够复制代码而没有任何错误。
我相信这个问题在StatsForecast库的最新更新中已经解决了，但是我仍然遇到了同样的错误。你能帮我解决这个问题吗？
我正在复制的代码来自：https://towardsdatascience.com/time-series-forecasting-with-statistical-models-f08dcd1d24d1
之前针对相同错误提出了类似问题，并更新了解决方案，但此错误在新解决方案之后也出现，随附的是问题的链接：在Python中进行多TS统计预测时，数据框定义出错

import random
from itertools import product
from IPython.display import display, Markdown
from multiprocessing import cpu_count
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from statsforecast import StatsForecast
from nixtlats.data.datasets.m4 import M4, M4Info

from statsforecast.models import CrostonClassic, CrostonSBA, CrostonOptimized, ADIDA, IMAPA, TSB
from statsforecast.models import SimpleExponentialSmoothing, SimpleExponentialSmoothingOptimized, SeasonalExponentialSmoothing, SeasonalExponentialSmoothingOptimized, Holt, HoltWinters
from statsforecast.models import HistoricAverage, Naive, RandomWalkWithDrift, SeasonalNaive, WindowAverage, SeasonalWindowAverage
from statsforecast.models import MSTL
from statsforecast.models import Theta, OptimizedTheta, DynamicTheta, DynamicOptimizedTheta
from statsforecast.models import AutoARIMA, AutoETS, AutoCES, AutoTheta

df = pd.read_excel ('C:/X/X/X/Data.xlsx',sheet_name='Transpose')
df.rename(columns = {'Row Labels':'Key'}, inplace=True) 
df['Key'] = df['Key'].astype(str)
df = pd.melt(df,id_vars='Key',value_vars=list(df.columns[1:]),var_name ='ds')
df.columns = df.columns.str.replace('Key', 'unique_id')
df.columns = df.columns.str.replace('value', 'y')
df["ds"] = pd.to_datetime(df["ds"],format='%Y-%m-%d')
df=df[["ds","unique_id","y"]]
df['unique_id'] = df['unique_id'].astype('object')
df = df.set_index('unique_id')
df.reset_index()

seasonality = 30 #Monthly data

models = [
    ADIDA,
    CrostonClassic(),
    CrostonSBA(),
    CrostonOptimized(),
    IMAPA,
    (TSB,0.3,0.2),
    MSTL,
    Theta,
    OptimizedTheta,
    DynamicTheta,
    DynamicOptimizedTheta,
    AutoARIMA,
    AutoETS,
    AutoCES,
    AutoTheta,
    HistoricAverage,
    Naive,
    RandomWalkWithDrift,
    (SeasonalNaive, seasonality),
    (SeasonalExponentialSmoothing, seasonality, 0.2),
    (SeasonalWindowAverage, seasonality, 2 * seasonality),
    (WindowAverage, 2 * seasonality)
    ]

fcst = StatsForecast(df=df, models=models, freq='MS', n_jobs=cpu_count())
%time forecasts = fcst.forecast(9)
forecasts.reset_index()

forecasts = forecasts.round(0)

forecasts.to_excel("C:/X/X/X/Forecast_Output.xlsx",sheet_name='Sheet1')

我正在使用的数据集如下所示：

{'Row Labels': {0: 'XYZ-912750', 1: 'XYZ-461356', 2: 'XYZ-150591', 3: 'XYZ-627885', 4: 'XYZ-582638', 5: 'XYZ-631691', 6: 'XYZ-409952', 7: 'XYZ-245245', 8: 'XYZ-230662', 9: 'XYZ-533388', 10: 'XYZ-248225', 11: 'XYZ-582912', 12: 'XYZ-486079', 13: 'XYZ-867685', 14: 'XYZ-873555', 15: 'XYZ-375397', 16: 'XYZ-428066', 17: 'XYZ-774244', 18: 'XYZ-602796', 19: 'XYZ-267306', 20: 'XYZ-576156', 21: 'XYZ-775994', 22: 'XYZ-226742', 23: 'XYZ-641711', 24: 'XYZ-928543', 25: 'XYZ-217200', 26: 'XYZ-971921', 27: 'XYZ-141388', 28: 'XYZ-848360', 29: 'XYZ-864999', 30: 'XYZ-821384', 31: 'XYZ-516339', 32: 'XYZ-462488', 33: 'XYZ-140964', 34: 'XYZ-225559', 35: 'XYZ-916534', 36: 'XYZ-389683', 37: 'XYZ-247803', 38: 'XYZ-718639', 39: 'XYZ-512944', 40: 'XYZ-727601', 41: 'XYZ-315757', 42: 'XYZ-764867', 43: 'XYZ-918344', 44: 'XYZ-430939', 45: 'XYZ-204784', 46: 'XYZ-415285', 47: 'XYZ-272089', 48: 'XYZ-812045', 49: 'XYZ-889257', 50: 'XYZ-275863', 51: 'XYZ-519930', 52: 'XYZ-102141', 53: 'XYZ-324473', 54: 'XYZ-999148', 55: 'XYZ-514915', 56: 'XYZ-932751', 57: 'XYZ-669878', 58: 'XYZ-233459', 59: 'XYZ-289984', 60: 'XYZ-150061', 61: 'XYZ-355028', 62: 'XYZ-881803', 63: 'XYZ-721426', 64: 'XYZ-522174', 65: 'XYZ-790172', 66: 'XYZ-744677', 67: 'XYZ-617017', 68: 'XYZ-982812', 69: 'XYZ-940695', 70: 'XYZ-119041', 71: 'XYZ-313844', 72: 'XYZ-868117', 73: 'XYZ-791717', 74: 'XYZ-100742', 75: 'XYZ-259687', 76: 'XYZ-688842', 77: 'XYZ-247326', 78: 'XYZ-360939', 79: 'XYZ-185017', 80: 'XYZ-244773', 81: 'XYZ-289058', 82: 'XYZ-477846', 83: 'XYZ-305072', 84: 'XYZ-828236', 85: 'XYZ-668927', 86: 'XYZ-616913', 87: 'XYZ-874876', 88: 'XYZ-371693', 89: 'XYZ-951238', 90: 'XYZ-371675', 91: 'XYZ-736997', 92: 'XYZ-922244', 93: 'XYZ-883225', 94: 'XYZ-267555', 95: 'XYZ-704013', 96: 'XYZ-874917', 97: 'XYZ-567402', 98: 'XYZ-167338', 99: 'XYZ-592671', 100: 'XYZ-130168', 101: 'XYZ-492522', 102: 'XYZ-696211', 103: 'XYZ-310469', 104: 'XYZ-973277', 105: 'XYZ-841356', 106: 'XYZ-389440', 107: 'XYZ-613876', 108: 'XYZ-662850', 109: 'XYZ-800625', 110: 'XYZ-500125', 111: 'XYZ-539949', 112: 'XYZ-576121', 113: 'XYZ-339006', 114: 'XYZ-247314', 115: 'XYZ-129049', 116: 'XYZ-980653', 117: 'XYZ-678520', 118: 'XYZ-584841', 119: 'XYZ-396755', 120: 'XYZ-409502', 121: 'XYZ-824561', 122: 'XYZ-825996', 123: 'XYZ-820540', 124: 'XYZ-264710', 125: 'XYZ-241176', 126: 'XYZ-491386', 127: 'XYZ-914132', 128: 'XYZ-496194', 129: 'XYZ-941615', 130: 'XYZ-765328', 131: 'XYZ-540602', 132: 'XYZ-222660', 133: 'XYZ-324367', 134: 'XYZ-583764', 135: 'XYZ-248478', 136: 'XYZ-379180', 137: 'XYZ-628462', 138: 'XYZ-454262'}, '2021-03-01': {0: 0, 1: 951, 2: 0, 3: 0, 4: 13, 5: 0, 6: 0, 7: 0, 8: 487, 9: 501, 10: 0, 11: 0, 12: 0, 13: 0, 14: 715, 15: 726, 16: 235, 17: 340, 18: 0, 19: 0, 20: 0, 21: 960, 22: 127, 23: 92, 24: 0, 25: 0, 26: 170, 27: 0, 28: 0, 29: 0, 30: 0, 31: 133, 32: 0, 33: 0, 34: 105, 35: 168, 36: 0, 37: 500, 38: 0, 39: 0, 40: 61, 41: 0, 42: 212, 43: 101, 44: 0, 45: 0, 46: 0, 47: 83, 48: 185, 49: 0, 50: 131, 51: 67, 52: 0, 53: 141, 54: 0, 55: 140, 56: 0, 57: 0, 58: 180, 59: 0, 60: 0, 61: 99, 62: 63, 63: 0, 64: 0, 65: 1590, 66: 0, 67: 0, 68: 15, 69: 113, 70: 0, 71: 0, 72: 0, 73: 54, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 108, 80: 0, 81: 62, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 29, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 69, 98: 0, 99: 0, 100: 0, 101: 62, 102: 30, 103: 42, 104: 0, 105: 0, 106: 0, 107: 67, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 52, 116: 36, 117: 0, 118: 110, 119: 0, 120: 44, 121: 0, 122: 102, 123: 0, 124: 71, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 77, 132: 56, 133: 0, 134: 0, 135: 103, 136: 0, 137: 0, 138: 53}, '2021-04-01': {0: 0, 1: 553, 2: 0, 3: 0, 4: 18, 5: 0, 6: 0, 7: 0, 8: 313, 9: 1100, 10: 0, 11: 0, 12: 0, 13: 0, 14: 336, 15: 856, 16: 216, 17: 415, 18: 0, 19: 0, 20: 0, 21: 1363, 22: 148, 23: 171, 24: 0, 25: 0, 26: 260, 27: 0, 28: 0, 29: 0, 30: 0, 31: 229, 32: 0, 33: 0, 34: 286, 35: 215, 36: 0, 37: 381, 38: 0, 39: 0, 40: 171, 41: 0, 42: 261, 43: 211, 44: 0, 45: 0, 46: 0, 47: 94, 48: 167, 49: 0, 50: 171, 51: 111, 52: 0, 53: 229, 54: 0, 55: 104, 56: 0, 57: 0, 58: 158, 59: 0, 60: 0, 61: 142, 62: 156, 63: 0, 64: 0, 65: 1152, 66: 0, 67: 0, 68: 19, 69: 160, 70: 0, 71: 0, 72: 0, 73: 50, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 146, 80: 0, 81: 25, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 69, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 49, 98: 0, 99: 0, 100: 0, 101: 22, 102: 46, 103: 48, 104: 0, 105: 0, 106: 0, 107: 60, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 24, 116: 51, 117: 0, 118: 112, 119: 0, 120: 73, 121: 0, 122: 155, 123: 0, 124: 57, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 59, 132: 62, 133: 0, 134: 0, 135: 132, 136: 0, 137: 0, 138: 70}, '2021-05-01': {0: 0, 1: 439, 2: 0, 3: 0, 4: 13, 5: 0, 6: 0, 7: 0, 8: 119, 9: 735, 10: 0, 11: 0, 12: 0, 13: 0, 14: 183, 15: 70, 16: 79, 17: 244, 18: 0, 19: 0, 20: 0, 21: 2842, 22: 30, 23: 76, 24: 0, 25: 0, 26: 95, 27: 0, 28: 0, 29: 0, 30: 0, 31: 38, 32: 0, 33: 0, 34: 197, 35: 114, 36: 0, 37: 140, 38: 0, 39: 0, 40: 91, 41: 0, 42: 82, 43: 83, 44: 0, 45: 0, 46: 0, 47: 35, 48: 126, 49: 0, 50: 83, 51: 101, 52: 0, 53: 94, 54: 0, 55: 100, 56: 0, 57: 0, 58: 89, 59: 0, 60: 0, 61: 94, 62: 112, 63: 0, 64: 0, 65: 1903, 66: 0, 67: 0, 68: 61, 69: 91, 70: 0, 71: 0, 72: 0, 73: 30, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 116, 80: 0, 81: 12, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 56, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0, 101: 20, 102: 42, 103: 35, 104: 0, 105: 0, 106: 0, 107: 59, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 0, 116: 27, 117: 0, 118: 45, 119: 0, 120: 49, 121: 0, 122: 129, 123: 0, 124: 58, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 41, 132: 41, 133: 0, 134: 0, 135: 61, 136: 0, 137: 0, 138: 38}, '2021-06-01': {0: 0, 1: 390, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 221, 9: 816, 10: 0, 11: 0, 12: 0, 13: 0, 14: 109, 15: 255, 16: 126, 17: 161, 18: 0, 19: 0, 20: 0, 21: 959, 22: 52, 23: 119, 24: 0, 25: 0, 26: 261, 27: 0, 28: 0, 29: 0, 30: 0, 31: 142, 32: 0, 33: 0, 34: 203, 35: 42, 36: 0, 37: 133, 38: 0, 39: 0, 40: 113, 41: 0, 42: 118, 43: 62, 44: 0, 45: 0, 46: 0, 47: 48, 48: 112, 49: 0, 50: 75, 51: 105, 52: 0, 53: 107, 54: 0, 55: 102, 56: 0, 57: 0, 58: 77, 59: 0, 60: 0, 61: 81, 62: 94, 63: 0, 64: 0, 65: 764, 66: 0, 67: 0, 68: 47, 69: 116, 70: 0, 71: 0, 72: 0, 73: 19, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 148, 80: 0, 81: 20, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 46, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 33, 98: 0, 99: 0, 100: 0, 101: 39, 102: 52, 103: 47, 104: 0, 105: 0, 106: 0, 107: 56, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 62, 116: 41, 117: 0, 118: 51, 119: 0, 120: 59, 121: 0, 122: 73, 123: 0, 124: 34, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 17, 132: 42, 133: 0, 134: 0, 135: 74, 136: 0, 137: 0, 138: 58}, '2021-07-01': {0: 0, 1: 349, 2: 0, 3: 0, 4: 11, 5: 0, 6: 0, 7: 0, 8: 222, 9: 418, 10: 0, 11: 0, 12: 0, 13: 0, 14: 104, 15: 57, 16: 92, 17: 118, 18: 0, 19: 0, 20: 0, 21: 2040, 22: 80, 23: 50, 24: 0, 25: 0, 26: 147, 27: 0, 28: 0, 29: 0, 30: 0, 31: 22, 32: 0, 33: 0, 34: 117, 35: 88, 36: 0, 37: 146, 38: 0, 39: 0, 40: 65, 41: 0, 42: 117, 43: 65, 44: 0, 45: 0, 46: 0, 47: 33, 48: 36, 49: 0, 50: 51, 51: 50, 52: 0, 53: 66, 54: 0, 55: 51, 56: 0, 57: 0, 58: 100, 59: 0, 60: 0, 61: 63, 62: 55, 63: 0, 64: 0, 65: 847, 66: 0, 67: 0, 68: 32, 69: 68, 70: 0, 71: 0, 72: 0, 73: 42, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 72, 80: 0, 81: 27, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 47, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 36, 98: 0, 99: 0, 100: 0, 101: 25, 102: 29, 103: 39, 104: 0, 105: 0, 106: 0, 107: 40, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 37, 116: 41, 117: 0, 118: 29, 119: 0, 120: 54, 121: 0, 122: 75, 123: 0, 124: 41, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 12, 132: 28, 133: 0, 134: 0, 135: 46, 136: 0, 137: 0, 138: 24}, '2021-08-01': {0: 0, 1: 402, 2: 0, 3: 0, 4: 14, 5: 0, 6: 0, 7: 0, 8: 138, 9: 373, 10: 0, 11: 0, 12: 0, 13: 0, 14: 133, 15: 107, 16: 69, 17: 116, 18: 0, 19: 0, 20: 0, 21: 1554, 22: 80, 23: 65, 24: 0, 25: 0, 26: 123, 27: 0, 28: 0, 29: 0, 30: 0, 31: 23, 32: 0, 33: 0, 34: 95, 35: 49, 36: 0, 37: 146, 38: 0, 39: 0, 40: 50, 41: 0, 42: 90, 43: 57, 44: 0, 45: 0, 46: 0, 47: 19, 48: 46, 49: 0, 50: 38, 51: 20, 52: 0, 53: 91, 54: 0, 55: 69, 56: 0, 57: 0, 58: 57, 59: 0, 60: 0, 61: 53, 62: 48, 63: 0, 64: 0, 65: 934, 66: 0, 67: 0, 68: 19, 69: 66, 70: 0, 71: 0, 72: 0, 73: 75, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 86, 80: 0, 81: 33, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 32, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 46, 98: 0, 99: 0, 100: 0, 101: 22, 102: 31, 103: 63, 104: 0, 105: 0, 106: 0, 107: 41, 108: 0, 109: 0, 110: 0, 111: 0, 112: 0, 113: 0, 114: 0, 115: 42, 116: 42, 117: 0, 118: 30, 119: 0, 120: 32, 121: 0, 122: 70, 123: 0, 124: 40, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 12, 132: 21, 133: 0, 134: 0, 135: 83, 136: 0, 137: 0, 138: 20}, '2021-09-01': {0: 0, 1: 560, 2: 55, 3: 496, 4: 11, 5: 0, 6: 0, 7: 0, 8: 77, 9: 309, 10: 45, 11: 257, 12: 0, 13: 0, 14: 87, 15: 179, 16: 61, 17: 79, 18: 65, 19: 144, 20: 307, 21: 840, 22: 52, 23: 41, 24: 108, 25: 156, 26: 113, 27: 0, 28: 30, 29: 27, 30: 0, 31: 59, 32: 0, 33: 0, 34: 66, 35: 53, 36: 70, 37: 42, 38: 0, 39: 26, 40: 38, 41: 0, 42: 50, 43: 11, 44: 209, 45: 56, 46: 52, 47: 18, 48: 47, 49: 0, 50: 58, 51: 32, 52: 0, 53: 76, 54: 0, 55: 45, 56: 0, 57: 63, 58: 95, 59: 0, 60: 0, 61: 33, 62: 45, 63: 0, 64: 96, 65: 249, 66: 0, 67: 0, 68: 0, 69: 73, 70: 0, 71: 30, 72: 0, 73: 41, 74: 0, 75: 0, 76: 37, 77: 22, 78: 0, 79: 68, 80: 18, 81: 47, 82: 0, 83: 0, 84: 0, 85: 79, 86: 0, 87: 75, 88: 40, 89: 39, 90: 35, 91: 0, 92: 0, 93: 0, 94: 40, 95: 0, 96: 0, 97: 44, 98: 30, 99: 46, 100: 0, 101: 33, 102: 40, 103: 31, 104: 0, 105: 17, 106: 15, 107: 32, 108: 15, 109: 0, 110: 58, 111: 63, 112: 0, 113: 0, 114: 0, 115: 42, 116: 35, 117: 19, 118: 55, 119: 0, 120: 25, 121: 0, 122: 47, 123: 0, 124: 37, 125: 16, 126: 24, 127: 124, 128: 67, 129: 0, 130: 0, 131: 28, 132: 20, 133: 0, 134: 0, 135: 34, 136: 0, 137: 26, 138: 28}, '2021-10-01': {0: 122, 1: 720, 2: 129, 3: 1135, 4: 11, 5: 0, 6: 0, 7: 85, 8: 122, 9: 280, 10: 100, 11: 159, 12: 0, 13: 0, 14: 87, 15: 115, 16: 40, 17: 32, 18: 236, 19: 176, 20: 322, 21: 334, 22: 113, 23: 49, 24: 133, 25: 119, 26: 136, 27: 0, 28: 74, 29: 56, 30: 38, 31: 83, 32: 0, 33: 0, 34: 65, 35: 88, 36: 75, 37: 68, 38: 52, 39: 36, 40: 44, 41: 11, 42: 40, 43: 13, 44: 198, 45: 244, 46: 130, 47: 23, 48: 44, 49: 0, 50: 62, 51: 49, 52: 0, 53: 92, 54: 0, 55: 14, 56: 0, 57: 83, 58: 58, 59: 0, 60: 0, 61: 44, 62: 42, 63: 39, 64: 37, 65: 132, 66: 0, 67: 0, 68: 49, 69: 57, 70: 0, 71: 40, 72: 112, 73: 28, 74: 102, 75: 0, 76: 56, 77: 17, 78: 22, 79: 37, 80: 48, 81: 0, 82: 14, 83: 13, 84: 48, 85: 84, 86: 0, 87: 104, 88: 81, 89: 34, 90: 49, 91: 0, 92: 0, 93: 42, 94: 101, 95: 41, 96: 11, 97: 74, 98: 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python-3.x

1条答案

按热度按时间
46scxncf

46scxncf1#

你必须示例化模型，因为它们是类。

from statsforecast import StatsForecast

from statsforecast.models import CrostonClassic, CrostonSBA, CrostonOptimized, ADIDA, IMAPA, TSB
from statsforecast.models import SimpleExponentialSmoothing, SimpleExponentialSmoothingOptimized, SeasonalExponentialSmoothing, SeasonalExponentialSmoothingOptimized, Holt, HoltWinters
from statsforecast.models import HistoricAverage, Naive, RandomWalkWithDrift, SeasonalNaive, WindowAverage, SeasonalWindowAverage
from statsforecast.models import MSTL
from statsforecast.models import Theta, OptimizedTheta, DynamicTheta, DynamicOptimizedTheta
from statsforecast.models import AutoARIMA, AutoETS, AutoCES, AutoTheta

seasonality = 12 #Monthly data

models = [
    ADIDA(),
    CrostonClassic(),
    CrostonSBA(),
    CrostonOptimized(),
    IMAPA(),
    TSB(0.3,0.2),
    Theta(season_length=seasonality),
    OptimizedTheta(season_length=seasonality),
    DynamicTheta(season_length=seasonality),
    DynamicOptimizedTheta(season_length=seasonality),
    AutoARIMA(season_length=seasonality),
    AutoCES(season_length=seasonality),
    AutoTheta(season_length=seasonality),
    HistoricAverage(),
    Naive(),
    RandomWalkWithDrift(),
    SeasonalNaive(season_length=seasonality),
    SeasonalExponentialSmoothing(season_length=seasonality, alpha=0.2),
]

fcst = StatsForecast(df=df, models=models, freq='MS', n_jobs=-1, 
                     fallback_model=SeasonalNaive(season_length=seasonality))
%time forecasts = fcst.forecast(9)
forecasts.reset_index()

forecasts = forecasts.round(0)

以下是修复错误的colab链接：https://colab.research.google.com/drive/1vwIImCoKzGvePbgFKidauV8sXimAvO48?usp=sharing

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