how to interpret the mean absolute value metric

[wilbertmatthew]

Hello,

I modified the example program to add the mean_absolute_error metric. The value returned is 16.392857142857146. How do I interpret the value in terms of the accuracy of the prediction. Its a rather high value.

Thanks

import numpy as np
import matplotlib.pyplot as plt
from pypsf import Psf
from sklearn.metrics import mean_absolute_error

plt.style.use("dark_background")

t_series = np.array([112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118,
115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140,
145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166,
171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194,
196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201,
204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229,
242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278,
284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306,
315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336,
340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337,
360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405,
417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432])
train = t_series[:-28]
test = t_series[-28:]

psf = Psf(cycle_length=12, apply_diff=True, diff_periods=12)
psf.fit(train)

pred = psf.predict(len(test))
err = mean_absolute_error(test, pred)
print("prediction err", err)

fig, ax = plt.subplots()
x_train = np.array(range(len(train)))
x_test_pred = np.array(range(len(test))) + x_train[-1]
ax.plot(x_train, train, c="lightblue")
ax.plot(x_test_pred, test, c="lightgreen")
ax.plot(x_test_pred, pred, c="tab:orange")
plt.legend(["Training", "Test", "Prediction"])
plt.tight_layout()
plt.show()