I am trying to find a way to predict the next number in a sequence of numbers. Normally I would go use liner regression for this, but as you can see, there are dates and one single column of data. There is no dependent variable, there is only an independent variable (Loans). Is there a simple way to predict what the next number could be, or maybe a range of two numbers, based on a know sequence of numbers? Also, is there a way to get the probability of the outcome, like 90% or 95% confident?
Here is my data.
Account Loans
2019 Aug 393.3
2020 Feb 383.2
2020 Mar 455.4
2020 Apr 542.0
2020 May 510.0
2020 Jun 483.5
2020 Jul 465.5
2020 Aug 448.2
Aug 12 451.1
Aug 19 447.5
Aug 26 442.3
Sep 02 444.7
Ultimately I would like to see something like: 443 to 445 with 95% confidence. Is that possible?
Take the average of the last n values (a). Subtract a from last number (l) as (s). The end result should be l-s or l+s.
def predict(arr, n):
l = arr[-1]
a = sum(arr[:n]) / n
s = abs(a - l)
lower_bounds = l - s
upper_bounds = l + s
return (upper_bounds, lower_bounds)
Consider using exponential smoothing from stats models
from statsmodels.tsa.api import SimpleExpSmoothing
def predict(arr, sl)
return SimpleExpSmoothing(arr).fit(smoothing_level=sl).fitted_values
statsmodels (Simple Exponential Smoothing): https://www.statsmodels.org/stable/examples/notebooks/generated/exponential_smoothing.html
Python Simple Exponential Smoothing
NumPy version of "Exponential weighted moving average", equivalent to pandas.ewm().mean()