CHI_Predictions.py

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import linregress
from scipy.optimize import curve_fit

data = [
    (1982, 165),
    (1983, 176),
    (1985, 170),
    (1986, 122),
    (1987, 166),
    (1988, 187),
    (1989, 199),
    (1990, 260),
    (1991, 240),
    (1992, 216),
    (1993, 330),
    (1994, 263),
    (1995, 228),
    (1996, 256),
    (1997, 234),
    (1998, 351),
    (1999, 312),
    (2000, 336),
    (2001, 352),
    (2002, 414),
    (2003, 468),
    (2004, 578),
    (2005, 372),
    (2006, 626),
    (2007, 840),
    (2008, 714),
    (2009, 1130),
    (2010, 1346),
    (2011, 1532),
    (2012, 1577),
    (2013, 1963),
    (2014, 2043),
    (2015, 2120),
    (2016, 2435),
    (2017, 2400),
    (2018, 2592)]

years = np.array([d[0] for d in data])
submissions = np.array([d[1] for d in data])

def func(x, a, b, c, d):
    return a + b * np.exp(c + d * x)

p0 = (0, 1, 0, 0.000001)
fit_data, covariance = curve_fit(func, years, submissions, p0, maxfev=100000)
x2 = np.linspace(years[0], years[-1] + 10, 500)
y2 = func(x2, *fit_data)

_, _, r, p, stdErr = linregress(submissions, func(years, *fit_data))
print("R-squared:", r ** 2)

print(years[-1] + 1, 'prediction:', int(func(years[-1] + 1, *fit_data)))

fig = plt.figure(frameon=False, facecolor='w')

plt.scatter(years, submissions, marker='o', s=30, linewidth=0, c='.2', zorder=2)
plt.plot(x2, y2, c=(.8, .3, .1), lw=1.5, zorder=1)

plt.xlim(1980, 2025)
plt.ylim(0, 10000)

plt.tight_layout()
plt.show()