Data
FOREX_audjpy-hour-Close

FOREX_audjpy-hour-Close

active ARFF Publicly available Visibility: public Uploaded 04-06-2019 by Jan van Rijn
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Source: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/ Edited by: Fabian Schut # Data Description This is the historical price data of the FOREX AUD/JPY from Dukascopy. One instance (row) is one candlestick of one hour. The whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend. The timezone of the feature Timestamp is Europe/Amsterdam. The class attribute is the direction of the mean of the Close_Bid and the Close_Ask of the following hour, relative to the Close_Bid and Close_Ask mean of the current minute. This means the class attribute is True when the mean Close price is going up the following hour, and the class attribute is False when the mean Close price is going down (or stays the same) the following hour. # Attributes `Timestamp`: The time of the current data point (Europe/Amsterdam) `Bid_Open`: The bid price at the start of this time interval `Bid_High`: The highest bid price during this time interval `Bid_Low`: The lowest bid price during this time interval `Bid_Close`: The bid price at the end of this time interval `Bid_Volume`: The number of times the Bid Price changed within this time interval `Ask_Open`: The ask price at the start of this time interval `Ask_High`: The highest ask price during this time interval `Ask_Low`: The lowest ask price during this time interval `Ask_Close`: The ask price at the end of this time interval `Ask_Volume`: The number of times the Ask Price changed within this time interval `Class`: Whether the average price will go up during the next interval

12 features

Class (target)nominal2 unique values
0 missing
Timestampdate43825 unique values
0 missing
Bid_Opennumeric19086 unique values
0 missing
Bid_Highnumeric18986 unique values
0 missing
Bid_Lownumeric19139 unique values
0 missing
Bid_Closenumeric19103 unique values
0 missing
Bid_Volumenumeric42812 unique values
0 missing
Ask_Opennumeric19182 unique values
0 missing
Ask_Highnumeric19135 unique values
0 missing
Ask_Lownumeric19120 unique values
0 missing
Ask_Closenumeric19119 unique values
0 missing
Ask_Volumenumeric42793 unique values
0 missing
Show all 12 features

62 properties

NumberOfInstances
43825
Number of instances (rows) of the dataset.
NumberOfFeatures
12
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
11
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
MeanSkewnessOfNumericAtts
2.01
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
87.54
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MeanStdDevOfNumericAtts
5796604991.77
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassPercentage
50.69
Percentage of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
0.31
Second quartile (Median) of skewness among attributes of the numeric type.
MajorityClassSize
22213
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.2
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
8.33
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
6.31
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
970.94
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
87.43
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
1435803692960.6
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
-0.87
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
91.67
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
6839
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
8.33
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
MaxSkewnessOfNumericAtts
14.57
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
6.31
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.31
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
63762646076
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
49.31
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
-0.87
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
4261.13
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
21612
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
87.53
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
99.64
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
130527609759.27
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
0.31
First quartile of skewness among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
6.31
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.49
Average class difference between consecutive instances.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
ClassEntropy
1
Entropy of the target attribute values.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
-0.87
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0
Number of attributes divided by the number of instances.
Show all 62 properties

10 tasks

Supervised Classification on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
Clustering on FOREX_audjpy-hour-Close
0 runs - estimation_procedure: 50 times Clustering
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