Apolsus
commented
7 months ago After several attempts, I determined that the problem was that there were some problems with auto-arima's search. Even for the same sequence, different best model search results may appear under different batch combinations. Here is a minimal example.
ts = [
[128, 88, 228, 357, 376, 448, 558, 521, 395, 211, 294, 253, 314, 272, 314, 295, 432, 565, 583, 473, 343, 195, 200, 255, 371, 189, 269, 272, 352, 374, 433, 296, 182, 187, 136, 82, 113, 105, 217, 203, 416, 504, 488, 519, 424, 263, 263, 220, 237, 160],
[214, 1351, 965, 848, 362, 273, 176, 187, 163, 201, 207, 173, 239, 137, 233, 231, 248, 160, 200, 222, 160,219, 173, 170, 225, 212, 216, 279, 197, 174, 163, 184, 149, 169, 178, 181, 212, 177, 172, 224, 234, 225, 204, 165, 178, 163, 140, 138, 125, 175],
]
ys = cupy.array(ts)
ys = ys.T
model = AutoARIMA(ys)
model.search(s = 0, d=(0, 1, 2), p=range(7), q=range(4), method="auto")
model.fit()
model.forecast(2)ts = [
[214, 1351, 965, 848, 362, 273, 176, 187, 163, 201, 207, 173, 239, 137, 233, 231, 248, 160, 200, 222, 160,219, 173, 170, 225, 212, 216, 279, 197, 174, 163, 184, 149, 169, 178, 181, 212, 177, 172, 224, 234, 225, 204, 165, 178, 163, 140, 138, 125, 175],
]
ys = cupy.array(ts)
ys = ys.T
model = AutoARIMA(ys)
model.search(s = 0, d=(0, 1, 2), p=range(7), q=range(4), method="auto")
model.fit()
model.forecast(2)
dantegd
Describe the bug I'm having some issues using cuML's Auto ARIMA model for large-scale time series forecasting. Specifically, when I tried to do a batch forecast on about 50,000 time series data, I got some unusually high values in the forecast results. However, when I select the unusually sequence from these data and predict it alone, I can get normal prediction results. Steps/Code to reproduce bug Hard to discribe it here, the data is private and large. One of the sequence is: [224, 69, 115, 94, 59, 63, 60, 52, 87, 118, 132, 149, 139, 89, 97, 115, 98, 82, 55, 77, 96, 133, 112, 92, 170, 128, 94, 84, 63, 75, 56, 77, 85, 121, 126, 101, 197, 98, 89, 71, 72, 30, 47, 73, 69, 106, 110, 128]
batch prediction gives '7405687891.374923'
Expected behavior The results of individual predictions and batch predictions should be the same. Environment details (please complete the following information):