NNETAR don't work with business days time series

[jcierocki]

While I was trying to forecast financial time series (which are defined only for business days) I couldn't actually use NNETAR model from fable package.

1. If I didn't fill the gaps I have always got:
   Warning: 1 error encountered for nnet
   [1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using tsibble::fill_gaps() if required.
2. If I filled the gaps using fill_gaps() I have got:
   Warning: 1 error encountered for nnet
   [1] missing values in object

[mitchelloharawild]

Please provide a minimal reproducible example that helps demonstrate the issues you're having.

[jcierocki]

My tsibble:

head(my_df) %>% kable()

y	date
18.423	2010-05-12
18.270	2010-05-13
18.168	2010-05-14
17.963	2010-05-17
18.064	2010-05-18
17.701	2010-05-19

my_df_fill <- fill_gaps(my_df)
head(my_df_fill) %>% kable()

y	date
18.423	2010-05-12
18.270	2010-05-13
18.168	2010-05-14
NA	2010-05-15
NA	2010-05-16
17.963	2010-05-17

index(my_df)
date

Case 1:

nn_model <- my_df %>% model(nn = NNETAR(y ~ AR()))

Error:
Warning: 1 error encountered for nn
[1] .data contains implicit gaps in time. You should check your data and convert implicit gaps into explicit missing values using tsibble::fill_gaps() if required.

Case 2:

nn_model <- my_df_fill %>% model(nn = NNETAR(y ~ AR()))

Error:
Warning: 1 error encountered for nn
[1] missing values in object

[mitchelloharawild]

Could you provide the data using dput(my_df), the first 6 rows isn't enough to reproduce this issue.

[jcierocki]

Here is dput(my_df) result:
structure(list(y = c(18.423, 18.27, 18.168, 17.963, 18.064, 17.701, 16.939, 17.22, 17.527, 16.887, 18.117, 17.923, 17.912, 17.912, 17.84, 17.886, 17.502, 17.399, 17.349, 17.757, 17.912, 17.86, 17.998, 18.038, 18.034, 18.095, 18.295, 18.669, 18.423, 18.371, 18.168, 18.172, 18.013, 17.912, 17.912, 18.219, 18.117, 18.019, 18.351, 18.32, 18.368, 18.475, 19.037, 19.343, 18.935, 18.935, 18.833, 19.125, 18.94, 19.165, 19.549, 19.293, 19.866, 19.657, 19.402, 19.958, 20.162, 20.316, 20.418, 20.266, 20.343, 20.214, 19.549, 19.523, 19.293, 19.601, 19.851, 19.964, 19.702, 19.774, 19.699, 19.948, 20.558, 20.306, 19.98, 19.532, 19.67, 19.753, 19.879, 19.742, 19.721, 20.102, 20.348, 20.163, 20.795, 21.023, 20.848, 20.906, 21.091, 21.164, 20.954, 21.19, 21.368, 21.638, 21.638, 21.113, 20.795, 20.954, 21.011, 20.954, 21.586, 21.059, 21.348, 21.206, 21.201, 20.795, 20.665, 20.785, 20.638, 20.848, 20.296, 20.217, 20.323, 20.485, 20.296, 20.163, 20.058, 19.742, 19.48, 19.85, 20.111, 19.907, 20.033, 19.742, 19.796, 20.086, 20.159, 20.274, 19.985, 19.48, 19.075, 18.852, 19.427, 19.38, 19.233, 19.28, 18.818, 18.896, 18.889, 18.584, 18.48, 18.685, 19.085, 19.158, 19.105, 19.321, 19.374, 19.053, 18.954, 18.981, 19.137, 19.306, 19.269, 19.011, 18.9, 18.795, 18.795, 18.779, 18.785, 18.768, 18.716, 18.795, 18.733, 18.717, 19.059, 18.539, 18.48, 18.48, 18.037, 18.231, 18.111, 17.954, 17.954, 18.32, 18.688, 18.427, 18.421, 18.353, 18.32, 18.237, 18.342, 18.033, 18.216, 18.163, 18.163, 18.263, 18.8, 18.553, 18.584, 18.606, 18.427, 18.32, 18.374, 18.269, 18.19, 18.059, 17.768, 18.069, 18.163, 18.516, 18.427, 18.374, 18.316, 18.289, 18.074, 17.854, 18.311, 18.427, 18.4, 18.632, 18.231, 18.111, 18.006, 18.059, 18, 17.885, 18.48, 19.079, 18.874, 18.584, 18.754, 18.531, 18.559, 18.879, 18.742, 18.463, 18.742, 19.053, 19.165, 19.191, 19.321, 19.427, 19.191, 19.18, 19.148, 19.191, 19.137, 19.169, 18.927, 19.059, 19.116, 19.132, 19.259, 19.402, 19.69, 19.637, 19.796, 19.532, 19.543, 19.69, 19.349, 19.77, 20.217, 20.269, 20.527, 20.691, 20.163, 19.706, 20.169, 20.533, 20.512, 20.558, 20.891, 20.674, 20.533, 20.475, 20.479, 20.533, 20.585, 20.533, 20.611, 20.564, 20.323, 19.558, 19.584, 19.753, 20.163, 20.006, 20.285, 20.427, 20.323, 20.217, 19.853, 20.006, 19.954, 20.102, 20.163, 19.742, 20.033, 20.058, 20.102, 19.842, 19.991, 19.85, 19.696, 19.532, 19.653, 19.69, 19.584, 19.306, 19.48, 19.543, 19.574, 19.837, 19.902, 19.969, 19.748, 19.902, 19.954, 19.902, 19.895, 19.269, 18.847, 18.539, 18.421, 17.764, 16.963, 17.696, 18.163, 18.611, 18.374, 17.586, 17.637, 17.705, 17.358, 17.716, 17.586, 17.22, 18.216, 18.417, 18.688, 18.638, 18.347, 17.954, 17.637, 18.374, 18.611, 17.69, 17.954, 17.9, 18.18, 18.258, 18.216, 18.311, 18.138, 18.263, 17.606, 17.889, 17.79, 18.1, 18.107, 18.112, 17.941, 17.371, 16.755, 17.03, 17.246, 17.456, 18.221, 18.061, 18.146, 17.714, 17.885, 17.885, 17.877, 18.197, 17.969, 18.328, 18.767, 18.909, 18.413, 19.235, 19.239, 19.222, 19.08, 19.535, 19.245, 19.17, 19.075, 18.283, 17.969, 18.112, 17.997, 18.453, 18.283, 18.397, 18.3, 18.055, 17.701, 17.714, 17.706, 17.656, 17.673, 18.192, 18.272, 18.516, 18.795, 18.608, 18.55, 17.714, 18.008, 17.736, 17.935, 17.758, 17.77, 17.661, 17.646, 17.793, 17.714, 17.598, 17.741, 17.77, 17.656, 17.656, 17.598), date = structure(c(14741, 14742, 14743, 14746, 14747, 14748, 14749, 14750, 14753, 14754, 14755, 14756, 14757, 14760, 14761, 14762, 14764, 14767, 14768, 14769, 14770, 14771, 14774, 14775, 14776, 14777, 14778, 14781, 14782, 14783, 14784, 14785, 14788, 14789, 14790, 14791, 14792, 14795, 14796, 14797, 14798, 14799, 14802, 14803, 14804, 14805, 14806, 14809, 14810, 14811, 14812, 14813, 14816, 14817, 14818, 14819, 14820, 14823, 14824, 14825, 14826, 14827, 14830, 14831, 14832, 14833, 14834, 14837, 14838, 14839, 14840, 14841, 14844, 14845, 14846, 14847, 14848, 14851, 14852, 14853, 14854, 14855, 14858, 14859, 14860, 14861, 14862, 14865, 14866, 14867, 14868, 14869, 14872, 14873, 14874, 14875, 14876, 14879, 14880, 14881, 14882, 14883, 14886, 14887, 14888, 14889, 14890, 14893, 14894, 14895, 14896, 14897, 14900, 14901, 14902, 14903, 14904, 14907, 14908, 14909, 14910, 14911, 14915, 14916, 14917, 14918, 14921, 14922, 14923, 14925, 14928, 14929, 14930, 14931, 14932, 14935, 14936, 14937, 14938, 14939, 14942, 14943, 14944, 14945, 14946, 14949, 14950, 14951, 14952, 14953, 14956, 14957, 14958, 14959, 14960, 14963, 14964, 14965, 14966, 14970, 14971, 14972, 14973, 14974, 14977, 14978, 14979, 14981, 14984, 14985, 14986, 14987, 14988, 14991, 14992, 14993, 14994, 14995, 14998, 14999, 15000, 15001, 15002, 15005, 15006, 15007, 15008, 15009, 15012, 15013, 15014, 15015, 15016, 15019, 15020, 15021, 15022, 15023, 15026, 15027, 15028, 15029, 15030, 15033, 15034, 15035, 15036, 15037, 15040, 15041, 15042, 15043, 15044, 15047, 15048, 15049, 15050, 15051, 15054, 15055, 15056, 15057, 15058, 15061, 15062, 15063, 15064, 15065, 15068, 15069, 15070, 15071, 15072, 15075, 15076, 15077, 15078, 15079, 15082, 15083, 15084, 15085, 15090, 15091, 15092, 15093, 15096, 15098, 15099, 15100, 15103, 15104, 15105, 15106, 15107, 15110, 15111, 15112, 15113, 15114, 15117, 15118, 15119, 15120, 15121, 15124, 15125, 15126, 15127, 15128, 15131, 15132, 15133, 15134, 15135, 15138, 15139, 15140, 15141, 15142, 15145, 15146, 15147, 15149, 15152, 15153, 15154, 15155, 15156, 15159, 15160, 15161, 15162, 15163, 15166, 15167, 15168, 15169, 15170, 15173, 15174, 15175, 15176, 15177, 15180, 15181, 15182, 15183, 15184, 15187, 15188, 15189, 15190, 15191, 15194, 15195, 15196, 15197, 15198, 15202, 15203, 15204, 15205, 15208, 15209, 15210, 15211, 15212, 15215, 15216, 15217, 15218, 15219, 15222, 15223, 15224, 15225, 15226, 15229, 15230, 15231, 15232, 15233, 15236, 15237, 15238, 15239, 15240, 15243, 15244, 15245, 15246, 15247, 15250, 15251, 15252, 15253, 15254, 15257, 15258, 15259, 15260, 15261, 15264, 15265, 15266, 15267, 15268, 15271, 15272, 15273, 15274, 15275, 15278, 15280, 15281, 15282, 15285, 15286, 15287, 15288, 15292, 15293, 15294, 15295, 15296, 15299, 15300, 15301, 15302, 15303, 15306, 15307, 15308, 15309, 15310, 15313, 15314, 15315, 15316, 15317, 15320, 15321, 15322, 15323, 15324, 15327, 15328, 15329, 15330, 15331, 15335, 15336, 15337, 15338 ), class = "Date")), row.names = c(NA, -415L), key = structure(list( .rows = structure(list(1:415), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame")), index = structure("date", ordered = TRUE), index2 = "date", interval = structure(list( year = 0, quarter = 0, month = 0, week = 0, day = 1, hour = 0, minute = 0, second = 0, millisecond = 0, microsecond = 0, nanosecond = 0, unit = 0), .regular = TRUE, class = c("interval", "vctrs_rcrd", "vctrs_vctr")), class = c("tbl_ts", "tbl_df", "tbl", "data.frame"))

[mitchelloharawild]

Thanks for the data. I've made an improvement to the NNETAR() training method to make it more robust to missing values, however I believe there may be an issue with ar.yw() for this particular dataset - so it doesn't work in this example yet.