Datasets:
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0 float32 -2.32 2.47 | 1 float32 -1.79 2.61 | 2 float32 -1.61 2.96 | 3 float32 -1.98 2.47 | 4 float32 -1.05 2.85 | 5 float32 -2.39 3.12 | 6 float32 -1.85 2.08 | OT float32 -2.22 2.22 | timestamp_idx int64 0 5.31k |
|---|---|---|---|---|---|---|---|---|
0.606785 | -0.361671 | 0.735367 | -1.164374 | 2.85189 | -1.861369 | -1.820047 | -0.124081 | 0 |
0.5709 | -0.367639 | 0.72963 | -1.170907 | 2.85189 | -1.838665 | -1.847258 | -0.113588 | 1 |
0.618423 | -0.252456 | 0.728914 | -1.027468 | 2.85189 | -1.736953 | -1.80513 | -0.07896 | 2 |
0.611634 | -0.206502 | 0.738951 | -1.007285 | 2.85189 | -1.756932 | -1.849738 | -0.082108 | 3 |
0.600966 | -0.111013 | 0.738235 | -0.953372 | 2.85189 | -1.768738 | -1.785181 | -0.066368 | 4 |
0.617453 | -0.106835 | 0.728914 | -0.966525 | 2.85189 | -1.81687 | -1.807629 | -0.008656 | 5 |
0.636851 | -0.057301 | 0.746852 | -0.906471 | 2.85189 | -1.818686 | -1.782683 | 0.022824 | 6 |
0.660127 | -0.088334 | 0.760539 | -0.921068 | 2.85189 | -1.825043 | -1.782683 | 0.037515 | 7 |
0.688253 | -0.01254 | 0.759824 | -0.830299 | 2.85189 | -1.825043 | -1.772654 | 0.070044 | 8 |
0.644609 | -0.087141 | 0.703212 | -0.923154 | 2.85189 | -1.828675 | -1.772654 | 0.043811 | 9 |
0.666916 | -0.131901 | 0.71034 | -0.882126 | 2.85189 | -1.829584 | -1.750045 | 0.07529 | 10 |
0.690193 | -0.161741 | 0.658616 | -0.931457 | 2.85189 | -1.85592 | -1.755077 | 0.096277 | 11 |
0.684374 | -0.149805 | 0.604756 | -0.914822 | 2.85189 | -1.835941 | -1.689226 | 0.0606 | 12 |
0.702801 | -0.194565 | 0.577064 | -0.986967 | 2.85189 | -1.869542 | -1.701969 | 0.073192 | 13 |
0.505919 | -0.134885 | 0.611711 | -0.887598 | 2.85189 | -1.855011 | -1.714656 | 0.003936 | 14 |
0.380807 | -0.099077 | 0.559841 | -0.838851 | 2.85189 | -1.822319 | -1.684122 | -0.139821 | 15 |
0.342983 | -0.085947 | 0.525619 | -0.802338 | 2.85189 | -1.783268 | -1.691778 | -0.108341 | 16 |
0.36432 | -0.087141 | 0.502514 | -0.845251 | 2.85189 | -1.736045 | -1.663651 | -0.12618 | 17 |
0.393415 | 0.038188 | 0.539277 | -0.793682 | 2.85189 | -1.726055 | -1.648248 | -0.103094 | 18 |
0.417662 | 0.060867 | 0.524943 | -0.815271 | 2.85189 | -1.768738 | -1.6095 | -0.082108 | 19 |
0.441908 | 0.046543 | 0.557784 | -0.870724 | 2.85189 | -1.788717 | -1.62244 | -0.029642 | 20 |
0.458396 | 0.059076 | 0.553659 | -0.846102 | 2.85189 | -1.804155 | -1.627616 | -0.068467 | 21 |
0.490402 | 0.06027 | 0.537906 | -0.840984 | 2.85189 | -1.824135 | -1.64052 | -0.04748 | 22 |
0.412813 | 0.139645 | 0.529715 | -0.768466 | 2.85189 | -1.825043 | -1.632774 | -0.052727 | 23 |
0.422511 | 0.181421 | 0.543383 | -0.754022 | 2.85189 | -1.814145 | -1.5861 | -0.040135 | 24 |
0.335224 | 0.163517 | 0.505229 | -0.774129 | 2.85189 | -1.828675 | -1.599112 | -0.117785 | 25 |
0.306128 | 0.091901 | 0.480888 | -0.831581 | 2.85189 | -1.825043 | -1.617265 | -0.139821 | 26 |
0.255695 | 0.124725 | 0.49101 | -0.840984 | 2.85189 | -1.81687 | -1.625028 | -0.124081 | 27 |
0.282851 | 0.139645 | 0.445323 | -0.835857 | 2.85189 | -1.773279 | -1.619852 | -0.128279 | 28 |
0.282851 | 0.112789 | 0.462062 | -0.840984 | 2.85189 | -1.78236 | -1.632774 | -0.129328 | 29 |
0.22078 | 0.145613 | 0.416037 | -0.820858 | 2.85189 | -1.776911 | -1.619852 | -0.244753 | 30 |
0.262484 | 0.127709 | 0.421339 | -0.834575 | 2.85189 | -1.78236 | -1.627616 | -0.208027 | 31 |
0.327465 | 0.142629 | 0.468099 | -0.826014 | 2.85189 | -1.771462 | -1.648248 | -0.173399 | 32 |
0.354621 | 0.193357 | 0.447999 | -0.789348 | 2.85189 | -1.827767 | -1.601718 | -0.157659 | 33 |
0.345892 | 0.259006 | 0.452683 | -0.744361 | 2.85189 | -1.822319 | -1.606894 | -0.169202 | 34 |
0.404084 | 0.257215 | 0.498448 | -0.73467 | 2.85189 | -1.846838 | -1.62244 | -0.158709 | 35 |
0.409903 | 0.229166 | 0.481564 | -0.754462 | 2.85189 | -1.890429 | -1.632774 | -0.153462 | 36 |
0.407963 | 0.127709 | 0.474832 | -0.798015 | 2.85189 | -1.970346 | -1.627616 | -0.174449 | 37 |
0.36432 | 0.121741 | 0.49642 | -0.781973 | 2.85189 | -1.961264 | -1.630204 | -0.179695 | 38 |
0.36044 | 0.10503 | 0.513372 | -0.817424 | 2.85189 | -1.96853 | -1.637932 | -0.176547 | 39 |
0.393415 | -0.039397 | 0.527667 | -0.879171 | 2.85189 | -2.003947 | -1.679017 | -0.184942 | 40 |
0.355591 | -0.122949 | 0.501839 | -0.910652 | 2.85189 | -2.028467 | -1.679017 | -0.17235 | 41 |
0.344922 | -0.195759 | 0.524943 | -0.853764 | 2.85189 | -2.008488 | -1.666221 | -0.178646 | 42 |
0.370139 | -0.134885 | 0.549545 | -0.85164 | 2.85189 | -2.008488 | -1.668773 | -0.165005 | 43 |
0.355591 | -0.194565 | 0.562594 | -0.882126 | 2.85189 | -2.065701 | -1.676448 | -0.167103 | 44 |
0.366259 | -0.173677 | 0.601289 | -0.89306 | 2.85189 | -2.050262 | -1.673896 | -0.177597 | 45 |
0.366259 | -0.319298 | 0.60268 | -0.905639 | 2.85189 | -2.070242 | -1.666221 | -0.148216 | 46 |
0.340073 | -0.365849 | 0.604756 | -0.953783 | 2.85189 | -2.111108 | -1.673896 | -0.163955 | 47 |
0.369169 | -0.425529 | 0.613111 | -0.970629 | 2.85189 | -2.118374 | -1.691778 | -0.194386 | 48 |
0.393415 | -0.373607 | 0.636148 | -0.966525 | 2.85189 | -2.105659 | -1.679017 | -0.182843 | 49 |
0.323585 | -0.322879 | 0.611711 | -0.945949 | 2.85189 | -2.118374 | -1.679017 | -0.157659 | 50 |
0.297399 | -0.287071 | 0.595735 | -0.893902 | 2.85189 | -2.111108 | -1.673896 | -0.176547 | 51 |
0.310007 | -0.365849 | 0.572234 | -0.890544 | 2.85189 | -2.154699 | -1.673896 | -0.20278 | 52 |
0.325525 | -0.364655 | 0.60268 | -0.923986 | 2.85189 | -2.16923 | -1.676448 | -0.226915 | 53 |
0.281881 | -0.460144 | 0.620076 | -0.941817 | 2.85189 | -2.180127 | -1.701969 | -0.267839 | 54 |
0.266364 | -0.421352 | 0.620771 | -0.916907 | 2.85189 | -2.216453 | -1.714656 | -0.25 | 55 |
0.237268 | -0.403448 | 0.632652 | -0.931457 | 2.85189 | -2.170138 | -1.707055 | -0.276233 | 56 |
0.232419 | -0.319895 | 0.626354 | -0.964478 | 2.85189 | -2.268217 | -1.722277 | -0.255246 | 57 |
0.215931 | -0.271554 | 0.625659 | -0.929381 | 2.85189 | -2.301818 | -1.724794 | -0.252099 | 58 |
0.234358 | -0.263198 | 0.624268 | -0.921068 | 2.85189 | -2.350858 | -1.742496 | -0.295121 | 59 |
0.284791 | -0.224406 | 0.637549 | -0.845251 | 2.85189 | -2.277298 | -1.737446 | -0.278332 | 60 |
0.309038 | -0.140853 | 0.664257 | -0.843118 | 2.85189 | -2.317257 | -1.732395 | -0.270986 | 61 |
0.321646 | -0.239326 | 0.671308 | -0.885494 | 2.852769 | -2.356307 | -1.734912 | -0.283579 | 62 |
0.36432 | -0.215454 | 0.694683 | -0.865224 | 2.852769 | -2.36448 | -1.727327 | -0.291973 | 63 |
0.426391 | -0.179646 | 0.686878 | -0.881285 | 2.85189 | -2.34541 | -1.704502 | -0.270986 | 64 |
0.407963 | -0.179646 | 0.677673 | -0.858011 | 2.85189 | -2.311808 | -1.69433 | -0.279381 | 65 |
0.407963 | -0.19755 | 0.699657 | -0.859293 | 2.85189 | -2.3109 | -1.681569 | -0.28148 | 66 |
0.388566 | -0.19755 | 0.726761 | -0.837569 | 2.85189 | -2.305451 | -1.689226 | -0.277282 | 67 |
0.401174 | -0.222616 | 0.7282 | -0.838 | 2.85189 | -2.344501 | -1.704502 | -0.278332 | 68 |
0.420572 | -0.173677 | 0.701792 | -0.785015 | 2.85189 | -2.333603 | -1.691778 | -0.268887 | 69 |
0.420572 | -0.176662 | 0.716754 | -0.779792 | 2.85189 | -2.329971 | -1.689226 | -0.264691 | 70 |
0.407963 | -0.19755 | 0.703212 | -0.789348 | 2.85189 | -2.350858 | -1.689226 | -0.26574 | 71 |
0.411843 | -0.24231 | 0.689003 | -0.812258 | 2.85189 | -2.372654 | -1.689226 | -0.264691 | 72 |
0.410873 | -0.225599 | 0.679798 | -0.813119 | 2.85189 | -2.391725 | -1.699416 | -0.284627 | 73 |
0.456456 | -0.200534 | 0.722472 | -0.785015 | 2.85189 | -2.363572 | -1.701969 | -0.273085 | 74 |
0.487492 | -0.16771 | 0.725331 | -0.779792 | 2.85189 | -2.268217 | -1.679017 | -0.244753 | 75 |
0.461306 | -0.216647 | 0.718898 | -0.816563 | 2.85189 | -2.318165 | -1.679017 | -0.232161 | 76 |
0.412813 | -0.21426 | 0.731773 | -0.823871 | 2.85189 | -2.312716 | -1.691778 | -0.231112 | 77 |
0.420572 | -0.223809 | 0.743973 | -0.802338 | 2.85189 | -2.34995 | -1.701969 | -0.241606 | 78 |
0.417662 | -0.206502 | 0.733213 | -0.710316 | 2.85189 | -2.34995 | -1.691778 | -0.242654 | 79 |
0.320676 | -0.22739 | 0.737511 | -0.68577 | 2.85189 | -2.350858 | -1.691778 | -0.307713 | 80 |
0.265394 | -0.21247 | 0.714619 | -0.665558 | 2.85189 | -2.356307 | -1.691778 | -0.328699 | 81 |
0.262484 | -0.195162 | 0.700362 | -0.636143 | 2.85189 | -2.354491 | -1.689226 | -0.315058 | 82 |
0.22078 | -0.185614 | 0.704631 | -0.658795 | 2.85189 | -2.354491 | -1.684122 | -0.381166 | 83 |
0.281881 | -0.170694 | 0.686878 | -0.658795 | 2.85189 | -2.334512 | -1.679017 | -0.357031 | 84 |
0.312917 | -0.175468 | 0.689003 | -0.653821 | 2.85189 | -2.329063 | -1.691778 | -0.337094 | 85 |
0.307098 | -0.069237 | 0.715324 | -0.609666 | 2.85189 | -2.336328 | -1.684122 | -0.338143 | 86 |
0.325525 | -0.021492 | 0.721043 | -0.553275 | 2.85189 | -2.323614 | -1.684122 | -0.338143 | 87 |
0.337164 | -0.01254 | 0.713904 | -0.5556 | 2.85189 | -2.319981 | -1.650819 | -0.316107 | 88 |
0.344922 | 0.038188 | 0.694683 | -0.417719 | 2.85189 | -2.266401 | -1.637932 | -0.34339 | 89 |
0.346862 | 0.028043 | 0.701077 | -0.376223 | 2.85189 | -2.287288 | -1.617265 | -0.324502 | 90 |
0.35947 | 0.057883 | 0.608234 | -0.37475 | 2.85189 | -2.12473 | -1.55472 | -0.365426 | 91 |
0.426391 | 0.073996 | 0.618685 | -0.377208 | 2.85189 | -2.126547 | -1.552114 | -0.357031 | 92 |
0.403114 | 0.03222 | 0.590886 | -0.388477 | 2.85189 | -2.072967 | -1.546847 | -0.339193 | 93 |
0.383717 | 0.050124 | 0.627755 | -0.398253 | 2.85189 | -2.073874 | -1.570446 | -0.336045 | 94 |
0.375958 | 0.11995 | 0.641055 | -0.417719 | 2.85189 | -2.1102 | -1.567821 | -0.328699 | 95 |
0.36432 | 0.114579 | 0.609625 | -0.421603 | 2.85189 | -2.130179 | -1.569404 | -0.339193 | 96 |
0.398265 | 0.116966 | 0.601289 | -0.513415 | 2.85189 | -2.156516 | -1.571488 | -0.328699 | 97 |
0.398265 | 0.118756 | 0.514057 | -0.484986 | 2.85189 | -2.08114 | -1.572531 | -0.328699 | 98 |
0.410873 | 0.133677 | 0.57292 | -0.457762 | 2.85189 | -2.066609 | -1.549472 | -0.3266 | 99 |
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in Data Studio
Exchange Rate Dataset
Part of the TS Arena benchmarking suite for time-series foundation models.
Description
Daily exchange rates of 8 countries (1990-2016)
Statistics
- Total samples: 7588
- Features: 8
- Train samples: 5311
- Validation samples: 759
- Test samples: 1518
- Frequency: 1 day
Preprocessing
- Standard normalization (zero mean, unit variance)
- Scaler fitted on training data only
- Train/Val/Test split: 70%/10%/20%
Usage
from datasets import load_dataset
# Load a specific split
train = load_dataset("ts-arena/exchange_rate", split="train")
val = load_dataset("ts-arena/exchange_rate", split="validation")
test = load_dataset("ts-arena/exchange_rate", split="test")
Source
License
MIT
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