Bottom Page

Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
 creating new time series based on giving mean, standard deviation and skewness
#1
I have a time series like

ts = [1,50,10,...,600]

I calculate the mean as

mean = 10

standard deviation = 100

skew = 5

I want to increase these parameters and then use it to generate new time series.

How can I do this in python?
Quote
#2
As far as I understood, you want to drop the original sample and generate a new one based on
obtained values of mean, std, skew. These are only three parameters, so it is better
to know (at least) class of allowed distributions; For example, normal distribution is fully determined
by mean and std values. So, if you knew that your data came from normal distribution, it
would be sufficient to just sample from the normal distribution with specific parameters,
e.g. using numpy.random.randn.

Take a look at MCMC approach, it is usually used when needed to approximate a distribution.
Quote

Top Page

Possibly Related Threads...
Thread Author Replies Views Last Post
  Time Series Production Process Problem Mzarour 0 84 Dec-06-2019, 06:44 PM
Last Post: Mzarour
  Keras: Time series classification midarq 0 150 Sep-25-2019, 09:03 AM
Last Post: midarq
  create 10 yearly blocks from time series using pandas Staph 1 246 Jul-23-2019, 12:01 PM
Last Post: Malt
  selecting customized seasons from monthly time series Staph 3 348 Jul-14-2019, 09:40 AM
Last Post: scidam
  [pandas]How to liner fit time series data and get linear fit equation and r square Sri 5 697 Apr-04-2019, 12:00 PM
Last Post: Sri
  Calculating median value from time data series mkaru 1 633 Aug-22-2018, 08:41 AM
Last Post: Mekire
  Time series honda_933 6 1,193 May-30-2018, 12:12 AM
Last Post: honda_933
Question Estimating standard deviation from DataSet jomardee 3 1,024 Jan-24-2018, 01:34 AM
Last Post: Larz60+
  Reduced Moving Averages for time series Data shivam_hbti 0 798 Oct-01-2017, 05:00 AM
Last Post: shivam_hbti

Forum Jump:


Users browsing this thread: 1 Guest(s)