Installing

Please install fastai2 according to the instructions.

Then install Fastseq by:

git clone https://github.com/takotab/fastseq.git
cd fastseq
pip install -e .

Please be aware that this extention on fastai2 is made for univarate data (The difference is explained here).

If you have tried it, please let me know. Even if you got stuck and werent able to install. You can send me an email, leave a comment in the (excelent) fastai timeseries forum, or make an issue in github. I would love to know what you think of libary any tips or comments. Do you work on these kind of data, would want more explanation of the model, would you like to see the approuch on multivariate ts. Where do think I should go with this.

Thank you

How to use

from fastai2.basics import *
from fastseq.all import *
from fastseq.nbeats.model import *
from fastseq.nbeats.learner import *
from fastseq.nbeats.callbacks import *

horizon, lookback = 7, 35    

Getting the data fastai style:

path = untar_data(URLs.m4_daily)
data = TSDataLoaders.from_folder(path, horizon = horizon, lookback = lookback, nrows = 300, step=3)

torch.Size([1, 1020])
Train:70707; Valid: 1200; Test 300

data.show_batch()

learn = nbeats_learner(data, season = lookback+horizon)   

from fastai2.callback.all import *
learn.lr_find()

SuggestedLRs(lr_min=0.10964782238006592, lr_steep=2.75422871709452e-06)

learn.fit_flat_cos(5, 2e-2)
learn.recorder.plot_loss()
learn.recorder.plot_sched()

epoch	train_loss	valid_loss	mae	smape	theta	b_loss	f_loss	f_smape	time
0	1.810993	1.584216	0.512324	0.814250	1.686225	nan	nan	0.110506	01:09
1	1.704236	1.457823	0.486268	0.774352	1.888560	nan	nan	0.105555	01:10
2	1.649843	1.489586	0.480835	0.770334	1.615496	nan	nan	0.108494	01:11
3	1.599777	1.418724	0.476658	0.752617	1.705645	nan	nan	0.103655	01:09
4	1.490466	1.363848	0.440083	0.709690	1.960501	nan	nan	0.102861	01:10

learn.show_results(0)

learn.show_results(1)

learn.fit_flat_cos(1,5e-4)
learn.recorder.plot_loss()
learn.recorder.plot_sched()

epoch	train_loss	valid_loss	mae	smape	theta	b_loss	f_loss	f_smape	time
0	1.424263	1.343198	0.420948	0.683976	1.796664	nan	nan	0.100251	01:28

Interperation

learn.n_beats_attention.means()

	trend0_0	trend0_1	seasonality1_0	seasonality1_1	seasonality1_2	seasonality1_3
theta_0_mean	1.2808248	0.12578407	-0.08569077	-0.06187745	0.1796875	1.0902514
theta_0_std	0.26519	0.08104574	0.66268957	0.4097473	0.12173311	0.6663508
theta_1_mean	0.0	-0.0008654712	0.32511836	0.2971403	-0.24606797	0.14985758
theta_1_std	0.0	0.0025685464	0.279926	0.2816404	0.298546	0.3349468
theta_2_mean	-0.0041518696	0.001206975	0.08984375	-0.11661168	-0.04089954	-0.062004503
theta_2_std	0.0010466882	0.00050746487	0.14342181	0.20864268	0.1293545	0.12580697
theta_3_mean	0.015779227	4.6875e-06	NaN	0.25079164	0.036999673	0.054956526
theta_3_std	0.15912743	1.4689242e-05	NaN	0.31157577	0.22201656	0.59105575
theta_4_mean	1.11071	0.023908217	NaN	NaN	-0.025488539	0.0049164467
theta_4_std	0.22121297	0.020769339	NaN	NaN	0.057381537	0.33296514
theta_5_mean	-0.6289774	0.27862015	NaN	NaN	NaN	-0.040294748
theta_5_std	0.7376536	0.43712172	NaN	NaN	NaN	0.12889466
att_mean	0.767369	0.638013	0.590672	0.59922	0.775596	0.999996
att_std	0.373486	0.444871	0.488639	0.487674	0.411862	1.40701e-05
theta_6_mean	NaN	1.1543798	NaN	NaN	NaN	NaN
theta_6_std	NaN	0.49151137	NaN	NaN	NaN	NaN
theta_7_mean	NaN	1.1794331	NaN	NaN	NaN	NaN
theta_7_std	NaN	0.5685613	NaN	NaN	NaN	NaN

interp = NBeatsInterpretation.from_learner(learn)

interp.plot_top_losses(3, combine_stack=True)