在 python 中使用 Prophet 为每个类别预测值

Question

我对在 Python 和 Prophet 中做时间序列还很陌生。我有一个包含变量商品代码、日期和销售数量的数据集。我正在尝试使用 python 中的 Prophet 预测每个月每篇文章的销量。

我尝试使用 for 循环为每篇文章执行预测，但我不确定如何在输出（预测）数据中显示文章类型以及如何将其直接从 "for loop" 写入文件.

df2 = df2.rename(columns={'Date of the document': 'ds','Quantity sold': 'y'})
for article in df2['Article bar code']:

    # set the uncertainty interval to 95% (the Prophet default is 80%)
    my_model = Prophet(weekly_seasonality= True, daily_seasonality=True,seasonality_prior_scale=1.0)
    my_model.fit(df2)
    future_dates = my_model.make_future_dataframe(periods=6, freq='MS')
    forecast = my_model.predict(future_dates)
return forecast

我想要如下所示的输出，并希望将其直接从 "for loop" 写入输出文件。

提前致谢。

Answer 1

通过 articletype 分隔数据框，然后尝试将所有预测值存储在字典中

def get_prediction(df):
    prediction = {}
    df = df.rename(columns={'Date of the document': 'ds','Quantity sold': 'y', 'Article bar code': 'article'})
    list_articles = df2.article.unique()

    for article in list_articles:
        article_df = df2.loc[df2['article'] == article]
        # set the uncertainty interval to 95% (the Prophet default is 80%)
        my_model = Prophet(weekly_seasonality= True, daily_seasonality=True,seasonality_prior_scale=1.0)
        my_model.fit(article_df)
        future_dates = my_model.make_future_dataframe(periods=6, freq='MS')
        forecast = my_model.predict(future_dates)
        prediction[article] = forecast
    return prediction

现在预测将对每种类型的文章进行预测。

Answer 2

我知道这是旧的，但我遇到了类似的问题，这对我有用：

df = pd.read_csv('file.csv')
df = pd.DataFrame(df)
df = df.rename(columns={'Date of the document': 'ds', 'Quantity sold': 'y', 'Article bar code': 'Article'})
#I filter first Articles bar codes with less than 3 records to avoid errors as prophet only works for 2+ records by group
df = df.groupby('Article').filter(lambda x: len(x) > 2)

df.Article = df.Article.astype(str)

final = pd.DataFrame(columns=['Article','ds','yhat'])

grouped = df.groupby('client_id')
for g in grouped.groups:
    group = grouped.get_group(g)
    m = Prophet()
    m.fit(group)
    future = m.make_future_dataframe(periods=365)
    forecast = m.predict(future)
    #I add a column with Article bar code
    forecast['Article'] = g
    #I concad all results in one dataframe
    final = pd.concat([final, forecast], ignore_index=True)

final.head(10)

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