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from statsmodels.tsa.ar_model import AutoReg as AR from scipy import interpolate

N = int(input())

data = [] for i in range(N): data.append(int(input().split()[1]))

leng = list(range(len(data)))

interpolation = interpolate.interp1d(leng, data, fill_value='extrapolate')

y = interpolation(range(len(data), len(data)+12))

for x in y: print(x)

from statsmodels.tsa.ar_model import AutoReg as AR

N = int(input())

data = [] for i in range(N): data.append(int(input().split()[1]))

model = AR(data, lags=1) fitted_model = model.fit() pred = fitted_model.predict()[:12]

for i in pred: print(i, end ='\n')

Don't waste your time on this problem. It is garbage. The editorial suggest that generate independent samples from the estimated distribution. Obviously it can only give something close to the expected solution by chance. Some of the top solutions simple return a list of values depending on first input line (there is only two test cases). Some other solution uses the interpolation function of the scipy module. They even produces negative values. So it is not clear how can they achived max points.

can i use Arima or fbprophet with this? last exercise i tried to use those libraries it output an error