Which forecast technique should I use to model a count time series with lot of zero's in python?

Question

I saw this, this and this questions, but maybe a new alternative has emerged so far.

I need to model a weekly time series that counts the number of enrolled students in a given school and grade. I have 5 years of historical data. The problem is that in general, 70% of my data is 0. Also, I need to apply it for 20 schools and 17 grades (so, the procedure, hopefully, should find the optimal number of parameters automatically).

Is there a way to do it? What I have tested so far:

Prophet and ARIMA provide negative values for the predict (and using y<0 -> y = 0 as a 'hack', does not help at all, because it increases the total number of enrolled students;
Croston's method is so smooth that does not detect any seasonality.
Tscount package in R is a possible approach, but my solution needs to be in Python (so I haven't tested).
A Poisson autoregressive (or a ZIP autoregressive) is an option (and the one that I am thinking to use). I haven't found any fully implemented package in Python, but I can reproduce this blog post.
LSTM with ReLU activation function is an option but I haven't tested it so far. The problem with this methodology is that although will not produce negative values, it will forecast real values (not integers).

Do you guys have any suggestions?

I need a methodology that is fast to estimate because I have 340 scenarios in total.
I will implement in Python.
Forecast integer values.
I am planning to forecast 4 steps ahead (or to forecast 1 step ahead as the last resource).
If I could include covariates would be great too.

This is my time series:

You can note that there is a 'long' period that there are 0 enrollments.

Two other pieces of information:

When I checked out my ACF plot, it detected lags 1, 2, 5, 6 (early lags) and later lags (51 and 52) as important (so, there is seasonality)
My data is not a white noise

My data itself:

from pandas import Timestamp
dd = pd.DataFrame.from_dict({'y': {0: 20, 1: 2, 2: 0, 3: 0, 4: 0, 5: 13, 6: 15, 7: 0, 8: 1, 9: 1, 10: 0, 11: 9, 12: 2, 13: 4, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 25, 52: 0, 53: 1, 54: 5, 55: 4, 56: 9, 57: 3, 58: 9, 59: 1, 60: 4, 61: 1, 62: 6, 63: 1, 64: 8, 65: 3, 66: 4, 67: 2, 68: 1, 69: 2, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 11, 100: 1, 101: 1, 102: 2, 103: 0, 104: 4, 105: 0, 106: 1, 107: 3, 108: 3, 109: 3, 110: 1, 111: 0, 112: 0, 113: 2, 114: 14, 115: 6, 116: 3, 117: 3, 118: 1, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 12, 155: 0, 156: 1, 157: 2, 158: 2, 159: 2, 160: 2, 161: 1, 162: 10, 163: 0, 164: 2, 165: 4, 166: 11, 167: 5, 168: 9, 169: 5, 170: 3, 171: 0, 172: 0, 173: 2, 174: 0, 175: 0, 176: 1, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 4, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 12, 215: 2, 216: 2, 217: 2, 218: 5, 219: 4, 220: 7, 221: 3, 222: 2, 223: 1}, 'Covariate2': {0: 1, 1: 1, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 0, 8: 1, 9: 1, 10: 0, 11: 1, 12: 1, 13: 1, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 1, 52: 0, 53: 1, 54: 1, 55: 1, 56: 1, 57: 1, 58: 1, 59: 1, 60: 1, 61: 1, 62: 1, 63: 1, 64: 1, 65: 1, 66: 1, 67: 1, 68: 1, 69: 1, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 1, 100: 1, 101: 1, 102: 1, 103: 0, 104: 1, 105: 0, 106: 1, 107: 1, 108: 1, 109: 1, 110: 1, 111: 0, 112: 0, 113: 1, 114: 1, 115: 1, 116: 1, 117: 1, 118: 1, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 1, 155: 0, 156: 1, 157: 1, 158: 1, 159: 1, 160: 1, 161: 1, 162: 1, 163: 0, 164: 1, 165: 1, 166: 1, 167: 1, 168: 1, 169: 1, 170: 1, 171: 0, 172: 0, 173: 1, 174: 0, 175: 0, 176: 1, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 1, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 1, 215: 1, 216: 1, 217: 1, 218: 1, 219: 1, 220: 1, 221: 1, 222: 1, 223: 1}, 'Covariate1': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 1, 9: 1, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 0, 52: 0, 53: 0, 54: 0, 55: 0, 56: 0, 57: 0, 58: 0, 59: 0, 60: 1, 61: 1, 62: 0, 63: 0, 64: 0, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0, 101: 0, 102: 0, 103: 0, 104: 0, 105: 0, 106: 0, 107: 0, 108: 1, 109: 1, 110: 1, 111: 0, 112: 0, 113: 0, 114: 0, 115: 0, 116: 0, 117: 0, 118: 0, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 0, 155: 0, 156: 0, 157: 0, 158: 0, 159: 0, 160: 0, 161: 1, 162: 1, 163: 0, 164: 0, 165: 0, 166: 0, 167: 0, 168: 0, 169: 0, 170: 0, 171: 0, 172: 0, 173: 0, 174: 0, 175: 0, 176: 0, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 0, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 1, 215: 0, 216: 0, 217: 0, 218: 0, 219: 0, 220: 0, 221: 0, 222: 0, 223: 0}, 'Date': {0: Timestamp('2016-11-14 00:00:00'), 1: Timestamp('2016-11-21 00:00:00'), 2: Timestamp('2016-11-28 00:00:00'), 3: Timestamp('2016-12-05 00:00:00'), 4: Timestamp('2016-12-12 00:00:00'), 5: Timestamp('2016-12-19 00:00:00'), 6: Timestamp('2016-12-26 00:00:00'), 7: Timestamp('2017-01-02 00:00:00'), 8: Timestamp('2017-01-09 00:00:00'), 9: Timestamp('2017-01-16 00:00:00'), 10: Timestamp('2017-01-23 00:00:00'), 11: Timestamp('2017-01-30 00:00:00'), 12: Timestamp('2017-02-06 00:00:00'), 13: Timestamp('2017-02-13 00:00:00'), 14: Timestamp('2017-02-20 00:00:00'), 15: Timestamp('2017-02-27 00:00:00'), 16: Timestamp('2017-03-06 00:00:00'), 17: Timestamp('2017-03-13 00:00:00'), 18: Timestamp('2017-03-20 00:00:00'), 19: Timestamp('2017-03-27 00:00:00'), 20: Timestamp('2017-04-03 00:00:00'), 21: Timestamp('2017-04-10 00:00:00'), 22: Timestamp('2017-04-17 00:00:00'), 23: Timestamp('2017-04-24 00:00:00'), 24: Timestamp('2017-05-01 00:00:00'), 25: Timestamp('2017-05-08 00:00:00'), 26: Timestamp('2017-05-15 00:00:00'), 27: Timestamp('2017-05-22 00:00:00'), 28: Timestamp('2017-05-29 00:00:00'), 29: Timestamp('2017-06-05 00:00:00'), 30: Timestamp('2017-06-12 00:00:00'), 31: Timestamp('2017-06-19 00:00:00'), 32: Timestamp('2017-06-26 00:00:00'), 33: Timestamp('2017-07-03 00:00:00'), 34: Timestamp('2017-07-10 00:00:00'), 35: Timestamp('2017-07-17 00:00:00'), 36: Timestamp('2017-07-24 00:00:00'), 37: Timestamp('2017-07-31 00:00:00'), 38: Timestamp('2017-08-07 00:00:00'), 39: Timestamp('2017-08-14 00:00:00'), 40: Timestamp('2017-08-21 00:00:00'), 41: Timestamp('2017-08-28 00:00:00'), 42: Timestamp('2017-09-04 00:00:00'), 43: Timestamp('2017-09-11 00:00:00'), 44: Timestamp('2017-09-18 00:00:00'), 45: Timestamp('2017-09-25 00:00:00'), 46: Timestamp('2017-10-02 00:00:00'), 47: Timestamp('2017-10-09 00:00:00'), 48: Timestamp('2017-10-16 00:00:00'), 49: Timestamp('2017-10-23 00:00:00'), 50: Timestamp('2017-10-30 00:00:00'), 51: Timestamp('2017-11-06 00:00:00'), 52: Timestamp('2017-11-13 00:00:00'), 53: Timestamp('2017-11-20 00:00:00'), 54: Timestamp('2017-11-27 00:00:00'), 55: Timestamp('2017-12-04 00:00:00'), 56: Timestamp('2017-12-11 00:00:00'), 57: Timestamp('2017-12-18 00:00:00'), 58: Timestamp('2017-12-25 00:00:00'), 59: Timestamp('2018-01-01 00:00:00'), 60: Timestamp('2018-01-08 00:00:00'), 61: Timestamp('2018-01-15 00:00:00'), 62: Timestamp('2018-01-22 00:00:00'), 63: Timestamp('2018-01-29 00:00:00'), 64: Timestamp('2018-02-05 00:00:00'), 65: Timestamp('2018-02-12 00:00:00'), 66: Timestamp('2018-02-19 00:00:00'), 67: Timestamp('2018-02-26 00:00:00'), 68: Timestamp('2018-03-05 00:00:00'), 69: Timestamp('2018-03-12 00:00:00'), 70: Timestamp('2018-03-19 00:00:00'), 71: Timestamp('2018-03-26 00:00:00'), 72: Timestamp('2018-04-02 00:00:00'), 73: Timestamp('2018-04-09 00:00:00'), 74: Timestamp('2018-04-16 00:00:00'), 75: 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Timestamp('2019-10-14 00:00:00'), 153: Timestamp('2019-10-21 00:00:00'), 154: Timestamp('2019-10-28 00:00:00'), 155: Timestamp('2019-11-04 00:00:00'), 156: Timestamp('2019-11-11 00:00:00'), 157: Timestamp('2019-11-18 00:00:00'), 158: Timestamp('2019-11-25 00:00:00'), 159: Timestamp('2019-12-02 00:00:00'), 160: Timestamp('2019-12-09 00:00:00'), 161: Timestamp('2019-12-16 00:00:00'), 162: Timestamp('2019-12-23 00:00:00'), 163: Timestamp('2019-12-30 00:00:00'), 164: Timestamp('2020-01-06 00:00:00'), 165: Timestamp('2020-01-13 00:00:00'), 166: Timestamp('2020-01-20 00:00:00'), 167: Timestamp('2020-01-27 00:00:00'), 168: Timestamp('2020-02-03 00:00:00'), 169: Timestamp('2020-02-10 00:00:00'), 170: Timestamp('2020-02-17 00:00:00'), 171: Timestamp('2020-02-24 00:00:00'), 172: Timestamp('2020-03-02 00:00:00'), 173: Timestamp('2020-03-09 00:00:00'), 174: Timestamp('2020-03-16 00:00:00'), 175: Timestamp('2020-03-23 00:00:00'), 176: Timestamp('2020-03-30 00:00:00'), 177: Timestamp('2020-04-06 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Timestamp('2020-10-05 00:00:00'), 204: Timestamp('2020-10-12 00:00:00'), 205: Timestamp('2020-10-19 00:00:00'), 206: Timestamp('2020-10-26 00:00:00'), 207: Timestamp('2020-11-02 00:00:00'), 208: Timestamp('2020-11-09 00:00:00'), 209: Timestamp('2020-11-16 00:00:00'), 210: Timestamp('2020-11-23 00:00:00'), 211: Timestamp('2020-11-30 00:00:00'), 212: Timestamp('2020-12-07 00:00:00'), 213: Timestamp('2020-12-14 00:00:00'), 214: Timestamp('2020-12-21 00:00:00'), 215: Timestamp('2020-12-28 00:00:00'), 216: Timestamp('2021-01-04 00:00:00'), 217: Timestamp('2021-01-11 00:00:00'), 218: Timestamp('2021-01-18 00:00:00'), 219: Timestamp('2021-01-25 00:00:00'), 220: Timestamp('2021-02-01 00:00:00'), 221: Timestamp('2021-02-08 00:00:00'), 222: Timestamp('2021-02-15 00:00:00'), 223: Timestamp('2021-02-22 00:00:00')}})

Maybe dups: https://stats.stackexchange.com/questions/198887/time-series-with-a-sequence-of-zeros, https://stats.stackexchange.com/questions/372125/forecasting-daily-time-series-sales-revenue-with-many-zero-entries — kjetil b halvorsen, Oct 25 '21 at 18:31
Why do you require integer forecasts? Put differently, which functional of the unknown future distribution do you want to elicit? An unbiased expectation forecast will not be integer, but a quantile (e.g., the median) will be. Essentially, this question boils down to what you later want to use the forecast for. — Stephan Kolassa, Oct 26 '21 at 05:40
I require an integer forecast because I am forecasting the # of enrolled students (integer number), that's why I thought this way. Also because I have a lot of 0 (any procedure like ARIMA or prophet will produce negative values. (If I am wrong, I can do it differently)... I want to do a forecast that contains the trend and seasonality (basically), plus covariate (if possible) — Guilherme Parreira, Oct 26 '21 at 12:01

Tylerr · Answer 1 · 2021-10-25T20:23:51.513

I would stick to something simple like the average of each period. It is clearly seasonal and as you noted some models won't like a lot of the zeros.

You could try a package I maintain ThymeBoost which will do a simple average for a given set of parameters.

We can define a simple model such as a median 'trend' plus classic (average each period) seasonality, and it seems to have reasonable results:

from pandas import Timestamp
from ThymeBoost import ThymeBoost as tb
dd = pd.DataFrame.from_dict({'date': {0: Timestamp('2016-11-14 00:00:00'), 1: Timestamp('2016-11-21 00:00:00'), 2: Timestamp('2016-11-28 00:00:00'), 3: Timestamp('2016-12-05 00:00:00'), 4: Timestamp('2016-12-12 00:00:00'), 5: Timestamp('2016-12-19 00:00:00'), 6: Timestamp('2016-12-26 00:00:00'), 7: Timestamp('2017-01-02 00:00:00'), 8: Timestamp('2017-01-09 00:00:00'), 9: Timestamp('2017-01-16 00:00:00'), 10: Timestamp('2017-01-23 00:00:00'), 11: Timestamp('2017-01-30 00:00:00'), 12: Timestamp('2017-02-06 00:00:00'), 13: Timestamp('2017-02-13 00:00:00'), 14: Timestamp('2017-02-20 00:00:00'), 15: Timestamp('2017-02-27 00:00:00'), 16: Timestamp('2017-03-06 00:00:00'), 17: Timestamp('2017-03-13 00:00:00'), 18: Timestamp('2017-03-20 00:00:00'), 19: Timestamp('2017-03-27 00:00:00'), 20: Timestamp('2017-04-03 00:00:00'), 21: Timestamp('2017-04-10 00:00:00'), 22: Timestamp('2017-04-17 00:00:00'), 23: Timestamp('2017-04-24 00:00:00'), 24: Timestamp('2017-05-01 00:00:00'), 25: Timestamp('2017-05-08 00:00:00'), 26: Timestamp('2017-05-15 00:00:00'), 27: Timestamp('2017-05-22 00:00:00'), 28: Timestamp('2017-05-29 00:00:00'), 29: Timestamp('2017-06-05 00:00:00'), 30: Timestamp('2017-06-12 00:00:00'), 31: Timestamp('2017-06-19 00:00:00'), 32: Timestamp('2017-06-26 00:00:00'), 33: Timestamp('2017-07-03 00:00:00'), 34: Timestamp('2017-07-10 00:00:00'), 35: Timestamp('2017-07-17 00:00:00'), 36: Timestamp('2017-07-24 00:00:00'), 37: Timestamp('2017-07-31 00:00:00'), 38: Timestamp('2017-08-07 00:00:00'), 39: Timestamp('2017-08-14 00:00:00'), 40: Timestamp('2017-08-21 00:00:00'), 41: Timestamp('2017-08-28 00:00:00'), 42: Timestamp('2017-09-04 00:00:00'), 43: Timestamp('2017-09-11 00:00:00'), 44: Timestamp('2017-09-18 00:00:00'), 45: Timestamp('2017-09-25 00:00:00'), 46: Timestamp('2017-10-02 00:00:00'), 47: Timestamp('2017-10-09 00:00:00'), 48: Timestamp('2017-10-16 00:00:00'), 49: Timestamp('2017-10-23 00:00:00'), 50: Timestamp('2017-10-30 00:00:00'), 51: Timestamp('2017-11-06 00:00:00'), 52: Timestamp('2017-11-13 00:00:00'), 53: Timestamp('2017-11-20 00:00:00'), 54: Timestamp('2017-11-27 00:00:00'), 55: Timestamp('2017-12-04 00:00:00'), 56: Timestamp('2017-12-11 00:00:00'), 57: Timestamp('2017-12-18 00:00:00'), 58: Timestamp('2017-12-25 00:00:00'), 59: Timestamp('2018-01-01 00:00:00'), 60: Timestamp('2018-01-08 00:00:00'), 61: Timestamp('2018-01-15 00:00:00'), 62: Timestamp('2018-01-22 00:00:00'), 63: Timestamp('2018-01-29 00:00:00'), 64: Timestamp('2018-02-05 00:00:00'), 65: Timestamp('2018-02-12 00:00:00'), 66: Timestamp('2018-02-19 00:00:00'), 67: Timestamp('2018-02-26 00:00:00'), 68: Timestamp('2018-03-05 00:00:00'), 69: Timestamp('2018-03-12 00:00:00'), 70: Timestamp('2018-03-19 00:00:00'), 71: Timestamp('2018-03-26 00:00:00'), 72: Timestamp('2018-04-02 00:00:00'), 73: Timestamp('2018-04-09 00:00:00'), 74: Timestamp('2018-04-16 00:00:00'), 75: Timestamp('2018-04-23 00:00:00'), 76: Timestamp('2018-04-30 00:00:00'), 77: Timestamp('2018-05-07 00:00:00'), 78: Timestamp('2018-05-14 00:00:00'), 79: Timestamp('2018-05-21 00:00:00'), 80: Timestamp('2018-05-28 00:00:00'), 81: Timestamp('2018-06-04 00:00:00'), 82: Timestamp('2018-06-11 00:00:00'), 83: Timestamp('2018-06-18 00:00:00'), 84: Timestamp('2018-06-25 00:00:00'), 85: Timestamp('2018-07-02 00:00:00'), 86: Timestamp('2018-07-09 00:00:00'), 87: Timestamp('2018-07-16 00:00:00'), 88: Timestamp('2018-07-23 00:00:00'), 89: Timestamp('2018-07-30 00:00:00'), 90: Timestamp('2018-08-06 00:00:00'), 91: Timestamp('2018-08-13 00:00:00'), 92: Timestamp('2018-08-20 00:00:00'), 93: Timestamp('2018-08-27 00:00:00'), 94: Timestamp('2018-09-03 00:00:00'), 95: Timestamp('2018-09-10 00:00:00'), 96: Timestamp('2018-09-17 00:00:00'), 97: Timestamp('2018-09-24 00:00:00'), 98: Timestamp('2018-10-01 00:00:00'), 99: Timestamp('2018-10-08 00:00:00'), 100: Timestamp('2018-10-15 00:00:00'), 101: Timestamp('2018-10-22 00:00:00'), 102: Timestamp('2018-10-29 00:00:00'), 103: Timestamp('2018-11-05 00:00:00'), 104: Timestamp('2018-11-12 00:00:00'), 105: Timestamp('2018-11-19 00:00:00'), 106: Timestamp('2018-11-26 00:00:00'), 107: Timestamp('2018-12-03 00:00:00'), 108: Timestamp('2018-12-10 00:00:00'), 109: Timestamp('2018-12-17 00:00:00'), 110: Timestamp('2018-12-24 00:00:00'), 111: Timestamp('2018-12-31 00:00:00'), 112: Timestamp('2019-01-07 00:00:00'), 113: Timestamp('2019-01-14 00:00:00'), 114: Timestamp('2019-01-21 00:00:00'), 115: Timestamp('2019-01-28 00:00:00'), 116: Timestamp('2019-02-04 00:00:00'), 117: Timestamp('2019-02-11 00:00:00'), 118: Timestamp('2019-02-18 00:00:00'), 119: Timestamp('2019-02-25 00:00:00'), 120: Timestamp('2019-03-04 00:00:00'), 121: Timestamp('2019-03-11 00:00:00'), 122: Timestamp('2019-03-18 00:00:00'), 123: Timestamp('2019-03-25 00:00:00'), 124: Timestamp('2019-04-01 00:00:00'), 125: Timestamp('2019-04-08 00:00:00'), 126: Timestamp('2019-04-15 00:00:00'), 127: Timestamp('2019-04-22 00:00:00'), 128: Timestamp('2019-04-29 00:00:00'), 129: Timestamp('2019-05-06 00:00:00'), 130: Timestamp('2019-05-13 00:00:00'), 131: Timestamp('2019-05-20 00:00:00'), 132: Timestamp('2019-05-27 00:00:00'), 133: Timestamp('2019-06-03 00:00:00'), 134: Timestamp('2019-06-10 00:00:00'), 135: Timestamp('2019-06-17 00:00:00'), 136: Timestamp('2019-06-24 00:00:00'), 137: Timestamp('2019-07-01 00:00:00'), 138: Timestamp('2019-07-08 00:00:00'), 139: Timestamp('2019-07-15 00:00:00'), 140: Timestamp('2019-07-22 00:00:00'), 141: Timestamp('2019-07-29 00:00:00'), 142: Timestamp('2019-08-05 00:00:00'), 143: Timestamp('2019-08-12 00:00:00'), 144: Timestamp('2019-08-19 00:00:00'), 145: Timestamp('2019-08-26 00:00:00'), 146: Timestamp('2019-09-02 00:00:00'), 147: Timestamp('2019-09-09 00:00:00'), 148: Timestamp('2019-09-16 00:00:00'), 149: Timestamp('2019-09-23 00:00:00'), 150: Timestamp('2019-09-30 00:00:00'), 151: Timestamp('2019-10-07 00:00:00'), 152: Timestamp('2019-10-14 00:00:00'), 153: Timestamp('2019-10-21 00:00:00'), 154: Timestamp('2019-10-28 00:00:00'), 155: Timestamp('2019-11-04 00:00:00'), 156: Timestamp('2019-11-11 00:00:00'), 157: Timestamp('2019-11-18 00:00:00'), 158: Timestamp('2019-11-25 00:00:00'), 159: Timestamp('2019-12-02 00:00:00'), 160: Timestamp('2019-12-09 00:00:00'), 161: Timestamp('2019-12-16 00:00:00'), 162: Timestamp('2019-12-23 00:00:00'), 163: Timestamp('2019-12-30 00:00:00'), 164: Timestamp('2020-01-06 00:00:00'), 165: Timestamp('2020-01-13 00:00:00'), 166: Timestamp('2020-01-20 00:00:00'), 167: Timestamp('2020-01-27 00:00:00'), 168: Timestamp('2020-02-03 00:00:00'), 169: Timestamp('2020-02-10 00:00:00'), 170: Timestamp('2020-02-17 00:00:00'), 171: Timestamp('2020-02-24 00:00:00'), 172: Timestamp('2020-03-02 00:00:00'), 173: Timestamp('2020-03-09 00:00:00'), 174: Timestamp('2020-03-16 00:00:00'), 175: Timestamp('2020-03-23 00:00:00'), 176: Timestamp('2020-03-30 00:00:00'), 177: Timestamp('2020-04-06 00:00:00'), 178: Timestamp('2020-04-13 00:00:00'), 179: Timestamp('2020-04-20 00:00:00'), 180: Timestamp('2020-04-27 00:00:00'), 181: Timestamp('2020-05-04 00:00:00'), 182: Timestamp('2020-05-11 00:00:00'), 183: Timestamp('2020-05-18 00:00:00'), 184: Timestamp('2020-05-25 00:00:00'), 185: Timestamp('2020-06-01 00:00:00'), 186: Timestamp('2020-06-08 00:00:00'), 187: Timestamp('2020-06-15 00:00:00'), 188: Timestamp('2020-06-22 00:00:00'), 189: Timestamp('2020-06-29 00:00:00'), 190: Timestamp('2020-07-06 00:00:00'), 191: Timestamp('2020-07-13 00:00:00'), 192: Timestamp('2020-07-20 00:00:00'), 193: Timestamp('2020-07-27 00:00:00'), 194: Timestamp('2020-08-03 00:00:00'), 195: Timestamp('2020-08-10 00:00:00'), 196: Timestamp('2020-08-17 00:00:00'), 197: Timestamp('2020-08-24 00:00:00'), 198: Timestamp('2020-08-31 00:00:00'), 199: Timestamp('2020-09-07 00:00:00'), 200: Timestamp('2020-09-14 00:00:00'), 201: Timestamp('2020-09-21 00:00:00'), 202: Timestamp('2020-09-28 00:00:00'), 203: Timestamp('2020-10-05 00:00:00'), 204: Timestamp('2020-10-12 00:00:00'), 205: Timestamp('2020-10-19 00:00:00'), 206: Timestamp('2020-10-26 00:00:00'), 207: Timestamp('2020-11-02 00:00:00'), 208: Timestamp('2020-11-09 00:00:00'), 209: Timestamp('2020-11-16 00:00:00'), 210: Timestamp('2020-11-23 00:00:00'), 211: Timestamp('2020-11-30 00:00:00'), 212: Timestamp('2020-12-07 00:00:00'), 213: Timestamp('2020-12-14 00:00:00'), 214: Timestamp('2020-12-21 00:00:00'), 215: Timestamp('2020-12-28 00:00:00'), 216: Timestamp('2021-01-04 00:00:00'), 217: Timestamp('2021-01-11 00:00:00'), 218: Timestamp('2021-01-18 00:00:00'), 219: Timestamp('2021-01-25 00:00:00'), 220: Timestamp('2021-02-01 00:00:00'), 221: Timestamp('2021-02-08 00:00:00'), 222: Timestamp('2021-02-15 00:00:00'), 223: Timestamp('2021-02-22 00:00:00')}, 'y': {0: 20, 1: 2, 2: 0, 3: 0, 4: 0, 5: 13, 6: 15, 7: 0, 8: 1, 9: 1, 10: 0, 11: 9, 12: 2, 13: 4, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 25, 52: 0, 53: 1, 54: 5, 55: 4, 56: 9, 57: 3, 58: 9, 59: 1, 60: 4, 61: 1, 62: 6, 63: 1, 64: 8, 65: 3, 66: 4, 67: 2, 68: 1, 69: 2, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 11, 100: 1, 101: 1, 102: 2, 103: 0, 104: 4, 105: 0, 106: 1, 107: 3, 108: 3, 109: 3, 110: 1, 111: 0, 112: 0, 113: 2, 114: 14, 115: 6, 116: 3, 117: 3, 118: 1, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 12, 155: 0, 156: 1, 157: 2, 158: 2, 159: 2, 160: 2, 161: 1, 162: 10, 163: 0, 164: 2, 165: 4, 166: 11, 167: 5, 168: 9, 169: 5, 170: 3, 171: 0, 172: 0, 173: 2, 174: 0, 175: 0, 176: 1, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 4, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 12, 215: 2, 216: 2, 217: 2, 218: 5, 219: 4, 220: 7, 221: 3, 222: 2, 223: 1}})
y = dd['y']
y.index = dd['date']
boosted_model = tb.ThymeBoost(verbose=1)

output = boosted_model.fit(y,
                           trend_estimator='median',
                           seasonal_estimator='classic',
                           seasonal_period=52,
                           global_cost='maicc',
                           fit_type='global',
                           )
predicted_output = boosted_model.predict(output, forecast_horizon=100)
boosted_model.plot_results(output, predicted_output)

The error bounds are off obviously but the actual predictions have a min of 0.

One issue with this will be that it isn't made for counts so you will have floats not integers so you will need to round, but since it is just a simple average you will never dip below 0 here.

EDIT with exogenous

Yes we can add exogenous but it will mess with our bounds since it is no longer a simple average but the average taking into account the extra features.

Feeding exogenous would be similar to prophet except you don't have to create the dataframe with the time series features just the future exogenous.

Here I will split it up into a basic train and test split. The exogenous estimator is a decision tree (you could also use 'ols' but tree looked better here) with depth of 1 and I switched the global_cost to mse as trees are iteration hungry in this setup.

import pandas as pd
from pandas import Timestamp
import matplotlib.pyplot as plt
from ThymeBoost import ThymeBoost as tb

dd = pd.DataFrame.from_dict({'y': {0: 20, 1: 2, 2: 0, 3: 0, 4: 0, 5: 13, 6: 15, 7: 0, 8: 1, 9: 1, 10: 0, 11: 9, 12: 2, 13: 4, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 25, 52: 0, 53: 1, 54: 5, 55: 4, 56: 9, 57: 3, 58: 9, 59: 1, 60: 4, 61: 1, 62: 6, 63: 1, 64: 8, 65: 3, 66: 4, 67: 2, 68: 1, 69: 2, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 11, 100: 1, 101: 1, 102: 2, 103: 0, 104: 4, 105: 0, 106: 1, 107: 3, 108: 3, 109: 3, 110: 1, 111: 0, 112: 0, 113: 2, 114: 14, 115: 6, 116: 3, 117: 3, 118: 1, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 12, 155: 0, 156: 1, 157: 2, 158: 2, 159: 2, 160: 2, 161: 1, 162: 10, 163: 0, 164: 2, 165: 4, 166: 11, 167: 5, 168: 9, 169: 5, 170: 3, 171: 0, 172: 0, 173: 2, 174: 0, 175: 0, 176: 1, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 4, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 12, 215: 2, 216: 2, 217: 2, 218: 5, 219: 4, 220: 7, 221: 3, 222: 2, 223: 1}, 'Covariate2': {0: 1, 1: 1, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 0, 8: 1, 9: 1, 10: 0, 11: 1, 12: 1, 13: 1, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 1, 52: 0, 53: 1, 54: 1, 55: 1, 56: 1, 57: 1, 58: 1, 59: 1, 60: 1, 61: 1, 62: 1, 63: 1, 64: 1, 65: 1, 66: 1, 67: 1, 68: 1, 69: 1, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 1, 100: 1, 101: 1, 102: 1, 103: 0, 104: 1, 105: 0, 106: 1, 107: 1, 108: 1, 109: 1, 110: 1, 111: 0, 112: 0, 113: 1, 114: 1, 115: 1, 116: 1, 117: 1, 118: 1, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 1, 155: 0, 156: 1, 157: 1, 158: 1, 159: 1, 160: 1, 161: 1, 162: 1, 163: 0, 164: 1, 165: 1, 166: 1, 167: 1, 168: 1, 169: 1, 170: 1, 171: 0, 172: 0, 173: 1, 174: 0, 175: 0, 176: 1, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 1, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 1, 215: 1, 216: 1, 217: 1, 218: 1, 219: 1, 220: 1, 221: 1, 222: 1, 223: 1}, 'Covariate1': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 1, 9: 1, 10: 0, 11: 0, 12: 0, 13: 0, 14: 0, 15: 0, 16: 0, 17: 0, 18: 0, 19: 0, 20: 0, 21: 0, 22: 0, 23: 0, 24: 0, 25: 0, 26: 0, 27: 0, 28: 0, 29: 0, 30: 0, 31: 0, 32: 0, 33: 0, 34: 0, 35: 0, 36: 0, 37: 0, 38: 0, 39: 0, 40: 0, 41: 0, 42: 0, 43: 0, 44: 0, 45: 0, 46: 0, 47: 0, 48: 0, 49: 0, 50: 0, 51: 0, 52: 0, 53: 0, 54: 0, 55: 0, 56: 0, 57: 0, 58: 0, 59: 0, 60: 1, 61: 1, 62: 0, 63: 0, 64: 0, 65: 0, 66: 0, 67: 0, 68: 0, 69: 0, 70: 0, 71: 0, 72: 0, 73: 0, 74: 0, 75: 0, 76: 0, 77: 0, 78: 0, 79: 0, 80: 0, 81: 0, 82: 0, 83: 0, 84: 0, 85: 0, 86: 0, 87: 0, 88: 0, 89: 0, 90: 0, 91: 0, 92: 0, 93: 0, 94: 0, 95: 0, 96: 0, 97: 0, 98: 0, 99: 0, 100: 0, 101: 0, 102: 0, 103: 0, 104: 0, 105: 0, 106: 0, 107: 0, 108: 1, 109: 1, 110: 1, 111: 0, 112: 0, 113: 0, 114: 0, 115: 0, 116: 0, 117: 0, 118: 0, 119: 0, 120: 0, 121: 0, 122: 0, 123: 0, 124: 0, 125: 0, 126: 0, 127: 0, 128: 0, 129: 0, 130: 0, 131: 0, 132: 0, 133: 0, 134: 0, 135: 0, 136: 0, 137: 0, 138: 0, 139: 0, 140: 0, 141: 0, 142: 0, 143: 0, 144: 0, 145: 0, 146: 0, 147: 0, 148: 0, 149: 0, 150: 0, 151: 0, 152: 0, 153: 0, 154: 0, 155: 0, 156: 0, 157: 0, 158: 0, 159: 0, 160: 0, 161: 1, 162: 1, 163: 0, 164: 0, 165: 0, 166: 0, 167: 0, 168: 0, 169: 0, 170: 0, 171: 0, 172: 0, 173: 0, 174: 0, 175: 0, 176: 0, 177: 0, 178: 0, 179: 0, 180: 0, 181: 0, 182: 0, 183: 0, 184: 0, 185: 0, 186: 0, 187: 0, 188: 0, 189: 0, 190: 0, 191: 0, 192: 0, 193: 0, 194: 0, 195: 0, 196: 0, 197: 0, 198: 0, 199: 0, 200: 0, 201: 0, 202: 0, 203: 0, 204: 0, 205: 0, 206: 0, 207: 0, 208: 0, 209: 0, 210: 0, 211: 0, 212: 0, 213: 0, 214: 1, 215: 0, 216: 0, 217: 0, 218: 0, 219: 0, 220: 0, 221: 0, 222: 0, 223: 0}, 'Date': {0: Timestamp('2016-11-14 00:00:00'), 1: Timestamp('2016-11-21 00:00:00'), 2: Timestamp('2016-11-28 00:00:00'), 3: Timestamp('2016-12-05 00:00:00'), 4: Timestamp('2016-12-12 00:00:00'), 5: Timestamp('2016-12-19 00:00:00'), 6: Timestamp('2016-12-26 00:00:00'), 7: Timestamp('2017-01-02 00:00:00'), 8: Timestamp('2017-01-09 00:00:00'), 9: Timestamp('2017-01-16 00:00:00'), 10: Timestamp('2017-01-23 00:00:00'), 11: Timestamp('2017-01-30 00:00:00'), 12: Timestamp('2017-02-06 00:00:00'), 13: Timestamp('2017-02-13 00:00:00'), 14: Timestamp('2017-02-20 00:00:00'), 15: Timestamp('2017-02-27 00:00:00'), 16: Timestamp('2017-03-06 00:00:00'), 17: Timestamp('2017-03-13 00:00:00'), 18: Timestamp('2017-03-20 00:00:00'), 19: Timestamp('2017-03-27 00:00:00'), 20: Timestamp('2017-04-03 00:00:00'), 21: Timestamp('2017-04-10 00:00:00'), 22: Timestamp('2017-04-17 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dd.index = dd['Date']
dd_train = dd.iloc[:156, :]
dd_test = dd.iloc[156:, :]

y = dd_train['y']
y_test = dd_test['y']
exo = dd_train[['Covariate1', 'Covariate2']]
future_exo = dd_test[['Covariate1', 'Covariate2']]

boosted_model = tb.ThymeBoost(verbose=1)
output = boosted_model.fit(y,
                           trend_estimator='median',
                           seasonal_estimator='classic',
                           exogenous_estimator='decision_tree',
                           tree_depth=1,
                           exogenous=exo,
                           seasonal_period=52,
                           global_cost='mse',
                           fit_type='global',
                           )
predicted_output = boosted_model.predict(output,
                                         forecast_horizon=len(y_test),
                                         future_exogenous=future_exo)
boosted_model.plot_results(output, predicted_output)

Looks ok but now we have some negatives so in order to get exactly what you want here let's put in a floor and round:

#Now we have some issues with our bounds so we will put in a floor and round
predictions = predicted_output['predictions'].clip(lower=0)
predictions = predictions.round()
plt.plot(y_test, label='actuals')
plt.plot(predictions, label='predicted')
plt.legend()
plt.show()

Tks Tylerr! I confess that I was thinking about using sth like this (an average of the recent and 1 year ago data). Can I also include covariates using your method? (do you have an example to show?). Because the large period with 0 is deterministic (and the one with spike two, but it can change every year). — Guilherme Parreira, Oct 25 '21 at 19:26
Yep edited my answer. TLDR: yes we can add covariates and use either a decision_tree (for any rounds >1 this becomes a boosted tree) or ols estimator for them. This will mess with our bounds though. — Tylerr, Oct 25 '21 at 20:26

Which forecast technique should I use to model a count time series with lot of zero's in python?

1 Answers1