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Time Series Forecasts - Popular Benchmark Datasets

Popular Datasets For Time Series Forecasting to use as Benchmark

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81

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About this Dataset

Time Series Forecasts - Popular Benchmark Datasets

Collection of popular datasets for time series forecasting.

Traffic: It contains hourly traffic data from 963 San Francisco freeway car lanes for short-term forecasting settings while it contains 862 car lanes for long-term forecasting. It is collected since 01/01/2015 with a sampling interval of every 1 hour.

Electricity: It contains electricity consumption of 370 clients for short-term forecasting while it contains electricity consumption of 321 clients for long-term forecasting. It is collected since 01/01/2011. The data sampling interval is every 15 minutes. (1)

COVID-19: It is about COVID-19 hospitalization in the U.S. state of California (CA) from 01/02/2020 to 31/12/2020 provided by the Johns Hopkins University with the sampling interval of every day.

Exchange: It contains the collection of the daily exchange rates of eight foreign countries including Australia, British, Canada, Switzerland, China, Japan, New Zealand, and Singapore ranging from 1990 to 2016 and the data sampling interval is every 1 day. (2)

Weather: It collects 21 meteorological indicators, such as humidity and air temperature, from the Weather Station of the Max Planck Biogeochemistry Institute in Germany in 2020. The data sampling interval is every 10 minutes. (3)

ETT: It is collected from two different electric transformers labeled with 1 and 2, and each of them contains 2 different resolutions (15 minutes and 1 hour) denoted with m and h. (4)

References
  1. https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014
  2. https://github.com/laiguokun/multivariate-time-series-data
  3. https://www.bgc-jena.mpg.de/wetter/
  4. https://github.com/zhouhaoyi/ETDataset
  5. "Frequency-domain {MLP}s are More Effective Learners in Time Series Forecasting", by Kun Yi and Qi Zhang and Wei Fan and Shoujin Wang and Pengyang Wang and Hui He and Ning An and Defu Lian and Longbing Cao and Zhendong Niu, Thirty-seventh Conference on Neural Information Processing Systems, 2023, https://openreview.net/forum?id=iif9mGCTfy

Tables

Covid

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.covid
  • 66.06 kB
  • 335 rows
  • 56 columns
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CREATE TABLE covid (
  "date" VARCHAR,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8,
  "n_9" DOUBLE  -- 9,
  "n_10" DOUBLE  -- 10,
  "n_11" DOUBLE  -- 11,
  "n_12" DOUBLE  -- 12,
  "n_13" DOUBLE  -- 13,
  "n_14" DOUBLE  -- 14,
  "n_15" DOUBLE  -- 15,
  "n_16" DOUBLE  -- 16,
  "n_17" DOUBLE  -- 17,
  "n_18" DOUBLE  -- 18,
  "n_19" DOUBLE  -- 19,
  "n_20" DOUBLE  -- 20,
  "n_21" DOUBLE  -- 21,
  "n_22" DOUBLE  -- 22,
  "n_23" DOUBLE  -- 23,
  "n_24" DOUBLE  -- 24,
  "n_25" DOUBLE  -- 25,
  "n_26" DOUBLE  -- 26,
  "n_27" DOUBLE  -- 27,
  "n_28" DOUBLE  -- 28,
  "n_29" DOUBLE  -- 29,
  "n_30" DOUBLE  -- 30,
  "n_31" DOUBLE  -- 31,
  "n_32" DOUBLE  -- 32,
  "n_33" DOUBLE  -- 33,
  "n_34" DOUBLE  -- 34,
  "n_35" DOUBLE  -- 35,
  "n_36" DOUBLE  -- 36,
  "n_37" DOUBLE  -- 37,
  "n_38" DOUBLE  -- 38,
  "n_39" DOUBLE  -- 39,
  "n_40" DOUBLE  -- 40,
  "n_41" DOUBLE  -- 41,
  "n_42" DOUBLE  -- 42,
  "n_43" DOUBLE  -- 43,
  "n_44" DOUBLE  -- 44,
  "n_45" DOUBLE  -- 45,
  "n_46" DOUBLE  -- 46,
  "n_47" DOUBLE  -- 47,
  "n_48" DOUBLE  -- 48,
  "n_49" DOUBLE  -- 49,
  "n_50" DOUBLE  -- 50,
  "n_51" DOUBLE  -- 51,
  "n_52" DOUBLE  -- 52,
  "n_53" DOUBLE  -- 53,
  "n_54" DOUBLE  -- 54,
  "n_55" DOUBLE  -- 55
);

Electricity

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.electricity
  • 13.32 MB
  • 26,304 rows
  • 322 columns
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CREATE TABLE electricity (
  "date" TIMESTAMP,
  "n_0" DOUBLE  -- 0,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8,
  "n_9" DOUBLE  -- 9,
  "n_10" DOUBLE  -- 10,
  "n_11" DOUBLE  -- 11,
  "n_12" DOUBLE  -- 12,
  "n_13" DOUBLE  -- 13,
  "n_14" DOUBLE  -- 14,
  "n_15" DOUBLE  -- 15,
  "n_16" DOUBLE  -- 16,
  "n_17" DOUBLE  -- 17,
  "n_18" DOUBLE  -- 18,
  "n_19" DOUBLE  -- 19,
  "n_20" DOUBLE  -- 20,
  "n_21" DOUBLE  -- 21,
  "n_22" DOUBLE  -- 22,
  "n_23" DOUBLE  -- 23,
  "n_24" DOUBLE  -- 24,
  "n_25" DOUBLE  -- 25,
  "n_26" DOUBLE  -- 26,
  "n_27" DOUBLE  -- 27,
  "n_28" DOUBLE  -- 28,
  "n_29" DOUBLE  -- 29,
  "n_30" DOUBLE  -- 30,
  "n_31" DOUBLE  -- 31,
  "n_32" DOUBLE  -- 32,
  "n_33" DOUBLE  -- 33,
  "n_34" DOUBLE  -- 34,
  "n_35" DOUBLE  -- 35,
  "n_36" DOUBLE  -- 36,
  "n_37" DOUBLE  -- 37,
  "n_38" DOUBLE  -- 38,
  "n_39" DOUBLE  -- 39,
  "n_40" DOUBLE  -- 40,
  "n_41" DOUBLE  -- 41,
  "n_42" DOUBLE  -- 42,
  "n_43" DOUBLE  -- 43,
  "n_44" DOUBLE  -- 44,
  "n_45" DOUBLE  -- 45,
  "n_46" DOUBLE  -- 46,
  "n_47" DOUBLE  -- 47,
  "n_48" DOUBLE  -- 48,
  "n_49" DOUBLE  -- 49,
  "n_50" DOUBLE  -- 50,
  "n_51" DOUBLE  -- 51,
  "n_52" DOUBLE  -- 52,
  "n_53" DOUBLE  -- 53,
  "n_54" DOUBLE  -- 54,
  "n_55" DOUBLE  -- 55,
  "n_56" DOUBLE  -- 56,
  "n_57" DOUBLE  -- 57,
  "n_58" DOUBLE  -- 58,
  "n_59" DOUBLE  -- 59,
  "n_60" DOUBLE  -- 60,
  "n_61" DOUBLE  -- 61,
  "n_62" DOUBLE  -- 62,
  "n_63" DOUBLE  -- 63,
  "n_64" DOUBLE  -- 64,
  "n_65" DOUBLE  -- 65,
  "n_66" DOUBLE  -- 66,
  "n_67" DOUBLE  -- 67,
  "n_68" DOUBLE  -- 68,
  "n_69" DOUBLE  -- 69,
  "n_70" DOUBLE  -- 70,
  "n_71" DOUBLE  -- 71,
  "n_72" DOUBLE  -- 72,
  "n_73" DOUBLE  -- 73,
  "n_74" DOUBLE  -- 74,
  "n_75" DOUBLE  -- 75,
  "n_76" DOUBLE  -- 76,
  "n_77" DOUBLE  -- 77,
  "n_78" DOUBLE  -- 78,
  "n_79" DOUBLE  -- 79,
  "n_80" DOUBLE  -- 80,
  "n_81" DOUBLE  -- 81,
  "n_82" DOUBLE  -- 82,
  "n_83" DOUBLE  -- 83,
  "n_84" DOUBLE  -- 84,
  "n_85" DOUBLE  -- 85,
  "n_86" DOUBLE  -- 86,
  "n_87" DOUBLE  -- 87,
  "n_88" DOUBLE  -- 88,
  "n_89" DOUBLE  -- 89,
  "n_90" DOUBLE  -- 90,
  "n_91" DOUBLE  -- 91,
  "n_92" DOUBLE  -- 92,
  "n_93" DOUBLE  -- 93,
  "n_94" DOUBLE  -- 94,
  "n_95" DOUBLE  -- 95,
  "n_96" DOUBLE  -- 96,
  "n_97" DOUBLE  -- 97,
  "n_98" DOUBLE  -- 98
);

Etth1

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.etth1
  • 317.52 kB
  • 17,420 rows
  • 8 columns
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CREATE TABLE etth1 (
  "date" TIMESTAMP,
  "hufl" DOUBLE,
  "hull" DOUBLE,
  "mufl" DOUBLE,
  "mull" DOUBLE,
  "lufl" DOUBLE,
  "lull" DOUBLE,
  "ot" DOUBLE
);

Ettm1

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.ettm1
  • 1.23 MB
  • 69,680 rows
  • 8 columns
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CREATE TABLE ettm1 (
  "date" TIMESTAMP,
  "hufl" DOUBLE,
  "hull" DOUBLE,
  "mufl" DOUBLE,
  "mull" DOUBLE,
  "lufl" DOUBLE,
  "lull" DOUBLE,
  "ot" DOUBLE
);

Exchange Rate

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.exchange_rate
  • 438.59 kB
  • 7,588 rows
  • 9 columns
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CREATE TABLE exchange_rate (
  "date" VARCHAR,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8
);

Traffic

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.traffic
  • 29.84 MB
  • 17,544 rows
  • 863 columns
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CREATE TABLE traffic (
  "date" TIMESTAMP,
  "n_0" DOUBLE  -- 0,
  "n_1" DOUBLE  -- 1,
  "n_2" DOUBLE  -- 2,
  "n_3" DOUBLE  -- 3,
  "n_4" DOUBLE  -- 4,
  "n_5" DOUBLE  -- 5,
  "n_6" DOUBLE  -- 6,
  "n_7" DOUBLE  -- 7,
  "n_8" DOUBLE  -- 8,
  "n_9" DOUBLE  -- 9,
  "n_10" DOUBLE  -- 10,
  "n_11" DOUBLE  -- 11,
  "n_12" DOUBLE  -- 12,
  "n_13" DOUBLE  -- 13,
  "n_14" DOUBLE  -- 14,
  "n_15" DOUBLE  -- 15,
  "n_16" DOUBLE  -- 16,
  "n_17" DOUBLE  -- 17,
  "n_18" DOUBLE  -- 18,
  "n_19" DOUBLE  -- 19,
  "n_20" DOUBLE  -- 20,
  "n_21" DOUBLE  -- 21,
  "n_22" DOUBLE  -- 22,
  "n_23" DOUBLE  -- 23,
  "n_24" DOUBLE  -- 24,
  "n_25" DOUBLE  -- 25,
  "n_26" DOUBLE  -- 26,
  "n_27" DOUBLE  -- 27,
  "n_28" DOUBLE  -- 28,
  "n_29" DOUBLE  -- 29,
  "n_30" DOUBLE  -- 30,
  "n_31" DOUBLE  -- 31,
  "n_32" DOUBLE  -- 32,
  "n_33" DOUBLE  -- 33,
  "n_34" DOUBLE  -- 34,
  "n_35" DOUBLE  -- 35,
  "n_36" DOUBLE  -- 36,
  "n_37" DOUBLE  -- 37,
  "n_38" DOUBLE  -- 38,
  "n_39" DOUBLE  -- 39,
  "n_40" DOUBLE  -- 40,
  "n_41" DOUBLE  -- 41,
  "n_42" DOUBLE  -- 42,
  "n_43" DOUBLE  -- 43,
  "n_44" DOUBLE  -- 44,
  "n_45" DOUBLE  -- 45,
  "n_46" DOUBLE  -- 46,
  "n_47" DOUBLE  -- 47,
  "n_48" DOUBLE  -- 48,
  "n_49" DOUBLE  -- 49,
  "n_50" DOUBLE  -- 50,
  "n_51" DOUBLE  -- 51,
  "n_52" DOUBLE  -- 52,
  "n_53" DOUBLE  -- 53,
  "n_54" DOUBLE  -- 54,
  "n_55" DOUBLE  -- 55,
  "n_56" DOUBLE  -- 56,
  "n_57" DOUBLE  -- 57,
  "n_58" DOUBLE  -- 58,
  "n_59" DOUBLE  -- 59,
  "n_60" DOUBLE  -- 60,
  "n_61" DOUBLE  -- 61,
  "n_62" DOUBLE  -- 62,
  "n_63" DOUBLE  -- 63,
  "n_64" DOUBLE  -- 64,
  "n_65" DOUBLE  -- 65,
  "n_66" DOUBLE  -- 66,
  "n_67" DOUBLE  -- 67,
  "n_68" DOUBLE  -- 68,
  "n_69" DOUBLE  -- 69,
  "n_70" DOUBLE  -- 70,
  "n_71" DOUBLE  -- 71,
  "n_72" DOUBLE  -- 72,
  "n_73" DOUBLE  -- 73,
  "n_74" DOUBLE  -- 74,
  "n_75" DOUBLE  -- 75,
  "n_76" DOUBLE  -- 76,
  "n_77" DOUBLE  -- 77,
  "n_78" DOUBLE  -- 78,
  "n_79" DOUBLE  -- 79,
  "n_80" DOUBLE  -- 80,
  "n_81" DOUBLE  -- 81,
  "n_82" DOUBLE  -- 82,
  "n_83" DOUBLE  -- 83,
  "n_84" DOUBLE  -- 84,
  "n_85" DOUBLE  -- 85,
  "n_86" DOUBLE  -- 86,
  "n_87" DOUBLE  -- 87,
  "n_88" DOUBLE  -- 88,
  "n_89" DOUBLE  -- 89,
  "n_90" DOUBLE  -- 90,
  "n_91" DOUBLE  -- 91,
  "n_92" DOUBLE  -- 92,
  "n_93" DOUBLE  -- 93,
  "n_94" DOUBLE  -- 94,
  "n_95" DOUBLE  -- 95,
  "n_96" DOUBLE  -- 96,
  "n_97" DOUBLE  -- 97,
  "n_98" DOUBLE  -- 98
);

Weather

@kaggle.giochelavaipiatti_time_series_forecasts_popular_benchm_3dd08d81.weather
  • 2.45 MB
  • 52,696 rows
  • 22 columns
Loading... 
CREATE TABLE weather (
  "date" TIMESTAMP,
  "p_mbar" DOUBLE  -- P (mbar),
  "t_degc" DOUBLE  -- T (degC),
  "tpot_k" DOUBLE  -- Tpot (K),
  "tdew_degc" DOUBLE  -- Tdew (degC),
  "rh" DOUBLE  -- Rh (%),
  "vpmax_mbar" DOUBLE  -- VPmax (mbar),
  "vpact_mbar" DOUBLE  -- VPact (mbar),
  "vpdef_mbar" DOUBLE  -- VPdef (mbar),
  "sh_g_kg" DOUBLE  -- Sh (g/kg),
  "h2oc_mmol_mol" DOUBLE  -- H2OC (mmol/mol),
  "rho_g_m_3" DOUBLE  -- Rho (g/m**3),
  "wv_m_s" DOUBLE  -- Wv (m/s),
  "max_wv_m_s" DOUBLE  -- Max. Wv (m/s),
  "wd_deg" DOUBLE  -- Wd (deg),
  "rain_mm" DOUBLE  -- Rain (mm),
  "raining_s" DOUBLE  -- Raining (s),
  "swdr_w_m" DOUBLE  -- SWDR (W/m�),
  "par_mol_m_s" DOUBLE  -- PAR (�mol/m�/s),
  "max_par_mol_m_s" DOUBLE  -- Max. PAR (�mol/m�/s),
  "tlog_degc" DOUBLE  -- Tlog (degC),
  "ot" DOUBLE
);

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