I want to predict the Y values which represents # of A-type clients/ time
using linear regression, where X values are time series data.
the code is
df1 = pd.DataFrame({'time': past_time_array, 'A_clients': client_A_array})
x_a = np.arange(len(past_time_array))
fit_A = np.polyfit(x_a, df1['A_clients'], 1)
fit_fn_A = np.poly1d(fit_A)
print df1
print "fitness function = %s" %fit_fn_A
result for print df1
is
A_clients time
0 0 2018-02-09 14:45:00
1 0 2018-02-09 14:46:00
2 1 2018-02-09 14:47:00
3 4 2018-02-09 14:48:00
4 4 2018-02-09 14:49:00
5 2 2018-02-09 14:50:00
6 2 2018-02-09 14:51:00
7 2 2018-02-09 14:52:00
8 2 2018-02-09 14:53:00
9 4 2018-02-09 14:54:00
10 1 2018-02-09 14:55:00
11 3 2018-02-09 14:56:00
12 4 2018-02-09 14:57:00
13 2 2018-02-09 14:58:00
14 4 2018-02-09 14:59:00
15 3 2018-02-09 15:00:00
16 1 2018-02-09 15:01:00
17 1 2018-02-09 15:02:00
18 0 2018-02-09 15:03:00
19 4 2018-02-09 15:04:00
20 1 2018-02-09 15:05:00
21 1 2018-02-09 15:06:00
22 4 2018-02-09 15:07:00
23 4 2018-02-09 15:08:00
result for print "fitness function = %s" %fit_fn_A
is
0.0001389 x + 2.213
Issue is that when I try to predict values like
predicted_ta = fit_fn_A(x_a[10])
print "predicted values = %f"%predicted_ta
it always gives me 2.213
which is c
value of y = mx+c
Best fit line is shown below
Regression line has some slope when I count #clietns every 2 mns instead of one
Values were getting predicted right, but earlier as I was calculating number of clients/ minute
and that graph is linear as shown above. So when I computed regression line for the number of clients/ 2 minutes
the fitness function gave the correct result.
You can not apply his model here. There is no dependence at all.
Try to calculate summarized number of clients (value[x] = sum(value[: x]). Usually it fits pretty good with log() model.
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