title: What characteristics does my dataset of time series need for machine learning forecasting with python?
body: <p>I want to create a dataset that can be used for forecasting with different machine learning algorithms (like LSTM). The dataset is a time series that contains one value per day. For my purpose it is important that the characteristics of the data changes from having a trend in the first 9 months to trend+seasonality for the last 3 months of a year (significantly higher demand around 1. and 15. of a month). </p>
  
  <p>First, I created random data with a trend for one period and added the higher demand in the last 3 months manually in Excel.</p>
  
  <p><a href="https://i.stack.imgur.com/jhP1v.png" rel="nofollow noreferrer">Plot of first period</a></p>
  
  <p>Now, I need to add the following periods and I wrote this code:</p>
  
  <pre><code>import matplotlib.pyplot as plt
  import math
  from random import seed
  from random import random
  from numpy.random import randint
  
  
  
  excel_file = 'Dataset 1.3.xls'
  datensatz =pd.read_excel(excel_file)
  
  
  demand5=pd.read_excel(excel_file,sheetname=4) 
  b=demand5['value']
  pnext=[]
  endeLast=1500
  
  
  ALL=[]
  plast=list(b)   
  for j in range(19,25):
      seed(j)
      pnext=[] 
      pnext.append(endeLast)
  
  
      if j % 2 ==0: 
          for i in range(0,365):
  
                  zahl=random()
                  if zahl &lt; 0.4:
                          movement = randint(30,150)
                  elif zahl &lt;0.7:
                          movement = randint(-70,30)
                  else:
                          movement = randint(150,250)
  
                  next = endeLast + plast[i]+ movement
  
                  pnext.append(next)
                  ALL.append(next)
  
                  plast[i]=next
  
      else:
          for i in range(0,365):
  
                  zahl=random()
                  if zahl &lt; 0.4:
                          movement = randint(50,250)
                  elif zahl &lt;0.7:
                          movement = randint(30,130)
                  else:
                          movement = randint(50,200)
  
                  next = endeLast + plast[i]+ movement
  
                  pnext.append(next)
                  ALL.append(next)
  
                  plast[i]=next
  
      endeLast=plast[-1] 
  
  plt.plot(ALL,'-')
  pyplot.show(ALL)
  </code></pre>
  
  <p>Is looks like this:
  <a href="https://i.stack.imgur.com/HdR5B.png" rel="nofollow noreferrer">plot of 6 new periods</a></p>
  
  <p>I have the feeling the periods look way too similar (even a simple Holt-Winter forecasting method could do the job). </p>
  
  <p>How can I change the characteristics of the dataset to make the periods less alike AND at the same time I still want the dataset to be useful as training data for my machine learning forecast?</p>
  
  <p>Or do you have any other suggestion on how to create more training data from one period of data?</p>
  
  <p>Thank you so much, I'm very new to python and any help is appreciated!</p>