-
-
Notifications
You must be signed in to change notification settings - Fork 1
/
paper.bib
440 lines (387 loc) · 16.5 KB
/
paper.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
@article{elvira2021performance,
title={On the performance of particle filters with adaptive number of particles},
author={Elvira, V{\'\i}ctor and Miguez, Joaqu{\'\i}n and Djuri{\'c}, Petar M},
journal={Statistics and Computing},
volume={31},
pages={1--18},
year={2021},
publisher={Springer}
}
@inbook{convtrans,
author = {Li, Shiyang and Jin, Xiaoyong and Xuan, Yao and Zhou, Xiyou and Chen, Wenhu and Wang, Yu-Xiang and Yan, Xifeng},
title = {Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting},
year = {2019},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer [1]. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self-attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length L, making directly modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self-attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only O(L(log L)2) memory cost, improving forecasting accuracy for time series with fine granularity and strong long-term dependencies under constrained memory budget. Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art.},
booktitle = {Proceedings of the 33rd International Conference on Neural Information Processing Systems},
articleno = {471},
numpages = {11}
}
@article{elvira2016adapting,
title={Adapting the number of particles in sequential Monte Carlo methods through an online scheme for convergence assessment},
author={Elvira, V{\'\i}ctor and M{\'\i}guez, Joaqu{\'\i}n and Djuri{\'c}, Petar M},
journal={IEEE Transactions on Signal Processing},
volume={65},
number={7},
pages={1781--1794},
year={2016},
publisher={IEEE}
}
@inproceedings{elvira2016online,
title={Online adaptation of the number of particles of SMC methods},
author={Elvira, Victor and M{\'\i}guez, Joaqu{\'\i}n and Djuri{\'c}, Petar M},
booktitle={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={4378--4382},
year={2016},
organization={IEEE}
}
@inproceedings{zaheer2017gan,
title={GAN connoisseur: Can GANs learn simple {1D} parametric distributions},
author={Zaheer, Manzil and Li, C-l and P{\'o}czos, Barnab{\'a}s and Salakhutdinov, Ruslan},
booktitle={Proceedings of the 31st Conference on Neural Information Processing Systems},
pages={1--6},
year={2017}
}
@article{goodfellow2014generative,
title={Generative adversarial nets},
author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
journal={Advances in neural information processing systems},
volume={27},
year={2014}
}
@inproceedings{arora2017generalization,
title={Generalization and equilibrium in generative adversarial nets ({GAN}s)},
author={Arora, Sanjeev and Ge, Rong and Liang, Yingyu and Ma, Tengyu and Zhang, Yi},
booktitle={International conference on machine learning},
pages={224--232},
year={2017},
organization={PMLR}
}
@inproceedings{arjovsky2017wasserstein,
title={Wasserstein generative adversarial networks},
author={Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
booktitle={International conference on machine learning},
pages={214--223},
year={2017},
organization={PMLR}
}
@article{li2017mmd,
title={{MMD GAN}: Towards deeper understanding of moment matching network},
author={Li, Chun-Liang and Chang, Wei-Cheng and Cheng, Yu and Yang, Yiming and P{\'o}czos, Barnab{\'a}s},
journal={Advances in neural information processing systems},
volume={30},
year={2017}
}
@article{arora2017gans,
title={Do {GAN}s actually learn the distribution? an empirical study},
author={Arora, Sanjeev and Zhang, Yi},
journal={arXiv preprint arXiv:1706.08224},
year={2017}
}
@inproceedings{santos2019learning,
title={Learning implicit generative models by matching perceptual features},
author={Santos, Cicero Nogueira dos and Mroueh, Youssef and Padhi, Inkit and Dognin, Pierre},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4461--4470},
year={2019}
}
@Book{Bill86,
Title = {Probability and Measure},
Author = {Patrick Billingsley},
Publisher = {John Wiley and Sons},
Year = {1986},
Edition = {Second}
}
@article{nowozin2016f,
title={f-{GAN}: Training generative neural samplers using variational divergence minimization},
author={Nowozin, Sebastian and Cseke, Botond and Tomioka, Ryota},
journal={Advances in neural information processing systems},
volume={29},
year={2016}
}
@inproceedings{mescheder2017adversarial,
title={Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks},
author={Mescheder, Lars and Nowozin, Sebastian and Geiger, Andreas},
booktitle={International conference on machine learning},
pages={2391--2400},
year={2017},
organization={PMLR}
}
@inproceedings{li2015generative,
title={Generative moment matching networks},
author={Li, Yujia and Swersky, Kevin and Zemel, Rich},
booktitle={International conference on machine learning},
pages={1718--1727},
year={2015},
organization={PMLR}
}
@article{gretton2012kernel,
title={A kernel two-sample test},
author={Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
journal={The Journal of Machine Learning Research},
volume={13},
number={1},
pages={723--773},
year={2012},
publisher={JMLR. org}
}
@inproceedings{rodriguez2022function,
title={Function-space inference with sparse implicit processes},
author={Rodríguez-Santana, Simon and Zaldivar, Bryan and Hernandez-Lobato, Daniel},
booktitle={International Conference on Machine Learning},
pages={18723--18740},
year={2022},
organization={PMLR}
}
@article{rosenblatt1952remarks,
title={Remarks on a multivariate transformation},
author={Rosenblatt, Murray},
journal={The annals of mathematical statistics},
volume={23},
number={3},
pages={470--472},
year={1952},
publisher={JSTOR}
}
@article{gouttes2021probabilistic,
title={Probabilistic time series forecasting with implicit quantile networks},
author={Gouttes, Ad{\`e}le and Rasul, Kashif and Koren, Mateusz and Stephan, Johannes and Naghibi, Tofigh},
journal={arXiv preprint arXiv:2107.03743},
year={2021}
}
@INPROCEEDINGS{rasul2020tempflow,
author = {Kashif Rasul and Abdul-Saboor Sheikh and Ingmar Schuster and Urs Bergmann and Roland Vollgraf},
title = {{M}ultivariate {P}robabilistic {T}ime {S}eries {F}orecasting via {C}onditioned {N}ormalizing {F}lows},
year = {2021},
url = {https://openreview.net/forum?id=WiGQBFuVRv},
booktitle = {International Conference on Learning Representations 2021},
}
@article{Sean18,
author = {Sean J. Taylor and Benjamin Letham},
title = {Forecasting at Scale},
journal = {The American Statistician},
volume = {72},
number = {1},
pages = {37-45},
year = {2018},
publisher = {Taylor & Francis},
doi = {10.1080/00031305.2017.1380080},
URL = { https://doi.org/10.1080/00031305.2017.1380080},
}
@inproceedings{Rangapuram18,
author = {Rangapuram, Syama Sundar and Seeger, Matthias W and Gasthaus, Jan and Stella, Lorenzo and Wang, Yuyang and Januschowski, Tim},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Deep State Space Models for Time Series Forecasting},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/5cf68969fb67aa6082363a6d4e6468e2-Paper.pdf},
volume = {31},
year = {2018}
}
@inproceedings{Yu2016,
author = {Yu, Hsiang-Fu and Rao, Nikhil and Dhillon, Inderjit S},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/85422afb467e9456013a2a51d4dff702-Paper.pdf},
volume = {29},
year = {2016}
}
@Article{wilsonbook,
author={Granville Tunnicliffe Wilson},
title={{Time Series Analysis: Forecasting and Control, 5th Edition , by George E. P. Box , Gwilym M. Jenkins , Gregory C. Reinsel and Greta M. Ljung , 2015 . Published by John Wiley and Sons Inc. , Hoboken, N}},
journal={Journal of Time Series Analysis},
year=2016,
volume={37},
number={5},
pages={709-711},
month={September},
keywords={},
doi={},
abstract={No abstract is available for this item.},
url={https://ideas.repec.org/a/bla/jtsera/v37y2016i5p709-711.html}
}
@InProceedings{pmlr-v139-rasul21a,
title = {{A}utoregressive {D}enoising {D}iffusion {M}odels for {M}ultivariate {P}robabilistic {T}ime {S}eries {F}orecasting},
author = {Rasul, Kashif and Seward, Calvin and Schuster, Ingmar and Vollgraf, Roland},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {8857--8868},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/rasul21a/rasul21a.pdf},
url = {http://proceedings.mlr.press/v139/rasul21a.html},
}
@inproceedings{arora2018gans,
title={Do {GAN}s learn the distribution? some theory and empirics},
author={Arora, Sanjeev and Risteski, Andrej and Zhang, Yi},
booktitle={International Conference on Learning Representations},
year={2018}
}
@article{moreno2023deep,
title={Deep autoregressive models with spectral attention},
author={Moreno-Pino, Fernando and Olmos, Pablo M and Art{\'e}s-Rodr{\'\i}guez, Antonio},
journal={Pattern Recognition},
volume={133},
pages={109014},
year={2023},
publisher={Elsevier}
}
@article{dziugaite2015training,
title={Training generative neural networks via maximum mean discrepancy optimization},
author={Dziugaite, Gintare Karolina and Roy, Daniel M and Ghahramani, Zoubin},
journal={arXiv preprint arXiv:1505.03906},
year={2015}
}
@article{uppal2019nonparametric,
title={Nonparametric density estimation \& convergence rates for {GAN}s under besov ipm losses},
author={Uppal, Ananya and Singh, Shashank and P{\'o}czos, Barnab{\'a}s},
journal={Advances in neural information processing systems},
volume={32},
year={2019}
}
@article{salinas2020deepar,
title={DeepAR: Probabilistic forecasting with autoregressive recurrent networks},
author={Salinas, David and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim},
journal={International Journal of Forecasting},
volume={36},
number={3},
pages={1181--1191},
year={2020},
publisher={Elsevier}
}
@misc{misc_electricityloaddiagrams20112014_321,
author = {Trindade,Artur},
title = {{ElectricityLoadDiagrams20112014}},
year = {2015},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C58C86}
}
@article{li2019enhancing,
title={Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting},
author={Li, Shiyang and Jin, Xiaoyong and Xuan, Yao and Zhou, Xiyou and Chen, Wenhu and Wang, Yu-Xiang and Yan, Xifeng},
journal={Advances in neural information processing systems},
volume={32},
year={2019}
}
@article{djuric2010assessment,
title={Assessment of nonlinear dynamic models by Kolmogorov--Smirnov statistics},
author={Djuric, Petar M and M{\'\i}guez, Joaqu{\'\i}n},
journal={IEEE transactions on signal processing},
volume={58},
number={10},
pages={5069--5079},
year={2010},
publisher={IEEE}
}
@book{martino2018independent,
title={Independent random sampling methods},
author={Martino, Luca and Luengo, David and M{\'\i}guez, Joaqu{\'\i}n},
year={2018},
publisher={Springer}
}
@article{kingma2014adam,
title={Adam: A method for stochastic optimization},
author={Kingma, Diederik P and Ba, Jimmy},
journal={arXiv preprint arXiv:1412.6980},
year={2014}
}
@article{salimans2016improved,
title={Improved techniques for training {GAN}s},
author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
journal={Advances in neural information processing systems},
volume={29},
year={2016}
}
@article{miyato2018spectral,
title={Spectral normalization for generative adversarial networks},
author={Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},
journal={arXiv preprint arXiv:1802.05957},
year={2018}
}
@article{mohamed2016learning,
title={Learning in implicit generative models},
author={Mohamed, Shakir and Lakshminarayanan, Balaji},
journal={arXiv preprint arXiv:1610.03483},
year={2016}
}
@misc{sohier_wind_generation,
title = {30 Years of European Wind Generation},
author = {Sohier, David},
howpublished = {\url{https://www.kaggle.com/datasets/sohier/30-years-of-european-wind-generation}},
year = {Year of Access}
}
@article{alexandrov2020gluonts,
title={Gluonts: Probabilistic and neural time series modeling in python},
author={Alexandrov, Alexander and Benidis, Konstantinos and Bohlke-Schneider, Michael and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Maddix, Danielle C and Rangapuram, Syama and Salinas, David and Schulz, Jasper and others},
journal={The Journal of Machine Learning Research},
volume={21},
number={1},
pages={4629--4634},
year={2020},
publisher={JMLRORG}
}
@inproceedings{zhou2021informer,
title={Informer: Beyond efficient transformer for long sequence time-series forecasting},
author={Zhou, Haoyi and Zhang, Shanghang and Peng, Jieqi and Zhang, Shuai and Li, Jianxin and Xiong, Hui and Zhang, Wancai},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={35},
number={12},
pages={11106--11115},
year={2021}
}
@article{wu2021autoformer,
title={Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting},
author={Wu, Haixu and Xu, Jiehui and Wang, Jianmin and Long, Mingsheng},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={22419--22430},
year={2021}
}
@inproceedings{zeng2023transformers,
title={Are transformers effective for time series forecasting?},
author={Zeng, Ailing and Chen, Muxi and Zhang, Lei and Xu, Qiang},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
volume={37},
number={9},
pages={11121--11128},
year={2023}
}
@inproceedings{deshpande2018generative,
title={Generative modeling using the sliced wasserstein distance},
author={Deshpande, Ishan and Zhang, Ziyu and Schwing, Alexander G},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={3483--3491},
year={2018}
}
@article{devroye2017measure,
title={On the measure of Voronoi cells},
author={Devroye, Luc and Gy{\"o}rfi, L{\'a}szl{\'o} and Lugosi, G{\'a}bor and Walk, Harro},
journal={Journal of Applied Probability},
volume={54},
number={2},
pages={394--408},
year={2017},
publisher={Cambridge University Press}
}
@article{scotta2023understanding,
title={Understanding and contextualising diffusion models},
author={Scotta, Stefano and Messina, Alberto},
journal={arXiv preprint arXiv:2302.01394},
year={2023}
}
@inproceedings{de2024training,
title={Training Implicit Generative Models via an Invariant Statistical Loss},
author={de Frutos, Jos{\'e} Manuel and Olmos, Pablo and Lopez, Manuel Alberto Vazquez and M{\'\i}guez, Joaqu{\'\i}n},
booktitle={International Conference on Artificial Intelligence and Statistics},
pages={2026--2034},
year={2024},
organization={PMLR}
}