Python中的时间序列趋势识别

Question

I have a CSV containing selling figures for various dates. Here is an example of the file:

DATE,       ARTICLENO, QUANTITY
2018-07-17, 101,       50
2018-07-16, 101,       55
2018-07-16, 105,       36
2018-07-15, 105,       23

I read this into a pandas dataframe and ran a basic kmeans-algorithm on this but i need more help.

Data description: The date column is the index of the dataframe and describes the date for the selling value. There are multiple tuples (Date-Quantity-ArticleNo) so there is a time series for each article number. Those can have different lengths and starting dates, which makes predicting and recognizing trends (eg good selling in summer or winter) even harder. The CSV is sorted by ArticleNo and Date.

Goal:

Cluster a given set of data from a csv and create labels for good selling articles in summer or winter (seasonal trends) and match future articles to them.

Here is what I did so far (currently i did not have date as index xet, but that is the goal):

from __future__ import absolute_import, division, print_function
import pandas as pd
import numpy as np
from matplotlib import pyplot as plp
from sklearn import preprocessing
from sklearn.cluster import KMeans
import sys

def extract_articles(data, article_numbers):
    return pd.DataFrame(
    [
        data[data['ARTICLENO'] == article_no]['QUANTITY'].values
        for article_no in article_numbers
    ]
 ).fillna(0)


def read_csv_file(file_name, number_of_lines):
    return pd.read_csv(file_name, parse_dates=['DATE'], 
nrows=number_of_lines)

def get_unique_article_numbers(data):
    return data['ARTICLENO'].unique()


def main():
    data = read_csv_file('statistic.csv', 400000)



    modeling_article_numbers = get_unique_article_numbers(data)
    print("Clustering on", len(modeling_article_numbers), "article numbers")
    modeling_data = extract_articles(data, modeling_article_numbers)
    modeling_data = modeling_data.iloc[:50, :]
    # 'switch' dataframe
    modeling_data = modeling_data.T
    modeling_data = modeling_data.pct_change().fillna(0)
    normalized_modeling_data = preprocessing.normalize(modeling_data, 
    norm='l2', axis=0)
    print(modeling_data)


    predicting_article_numbers = [30079229, 30079854, 30086845]
    predicting_article_data = extract_articles(data, 
    predicting_article_numbers)
    predicting_article_data = predicting_article_data.pct_change().fillna(0)
    normalized_predicting_article_data = preprocessing.normalize( 
    predicting_article_data, norm='l2'
    )


    kmeans = KMeans(n_clusters=5, 
    random_state=0).fit(normalized_modeling_data)
    print(kmeans.labels_)
    # for data, article_no in [
        # (normalized_predicting_article_data, 430079229),
        # (normalized_predicting_article_data, 430079854),
        # (modeling_data, 430074590),
        # ]:
    # print('Predicting article {0}'.format(article_no))
    # print(kmeans.predict([data[0]]))

    for i, cluster_center in enumerate(kmeans.cluster_centers_):
         plp.plot(cluster_center, label='Center {0}'.format(i))
    plp.legend(loc='best')
    plp.title(('Cluster based on ' + str(len(modeling_article_numbers)) + ' 
    article numbers'))
    plp.show()


 main()

I transposed the dataframe, beacause it did not contain the series for each article number along the axis 1. My question is: How can i get the 'description' of the label? Can i name them? Maybe kmeans is the wrong algorithm for my intentions?

Answer 1

have you tried making each article a row in your dataset?

I'm not sure if you did after reading your question.

After you did that you can aggregate your date eg as quantity per week. If you have more than one year data make it average quantity per week. So you get a table with 52 Features {week 1 : sold 500; week 2 : sold 520 ...} for every article.

I dont think k-means is what you are looking for because you know pretty well what you want and that makes you a good "teacher" for your algorithm, ergo: use supervised algortihms. Therefore you need to lable at least some (at best all) of your aggregated product data by hand, but it should be worth the work due to better results.

Also you could look into Time-Series Sesonality Analysis / Time Series decomposition.

Anyway if you are familiar with sci-kit learn i would give the supervised algorithms (Decision Trees, Random Forest, SVM, MLPClassifier ...) a chance, might be way easier to accomplish.