The ability to analyze time series data at scale is critical for the success of finance and IoT applications based on Spark. Flint is the Two Sigma’s implementation of highly optimized time series operations in Spark. It performs truly parallel and rich analyses on time series data by taking advantage of the natural ordering in time series data to provide locality-based optimizations. Flint is an open source library for Spark based around the TimeSeriesRDD
, a time series aware data structure, and a collection of time series utility and analysis functions that use TimeSeriesRDD
s. Unlike DataFrame
and Dataset
, Flint’s TimeSeriesRDD
s can leverage the existing ordering properties of datasets at rest and the fact that almost all data manipulations and analysis over these datasets respect their temporal ordering properties. It differs from other time series efforts in Spark in its ability to efficiently compute across panel data or on large scale high frequency data.
To build this sbt project, one could simply do
sbt assembly
The entry point into all functionalities for time series analysis in Flint is the TimeSeriesRDD
class or object. In high level, a TimeSeriesRDD
contains an OrderedRDD
which could be used to represent a sequence of ordering key-value pairs. A TimeSeriesRDD
uses Long
to represent timestamps in nanoseconds since epoch as keys and InternalRow
s as values for OrderedRDD
to represent a time series data set.
Applications can create a TimeSeriesRDD
from an existing RDD
, from an OrderedRDD
, from a DataFrame
, or from a single csv file.
As an example, the following creates a TimeSeriesRDD
from a gzipped CSV file with header and specific datetime format.
import com.twosigma.flint.timeseries.CSV
val tsRdd = CSV.from(
sqlContext,
"file://foo/bar/data.csv",
header = true,
dateFormat = "yyyyMMdd HH:mm:ss.SSS",
codec = "gzip",
sorted = true
)
To create a TimeSeriesRDD
from a DataFrame
, you have to make sure the DataFrame
contains a column named "time" of type LongType
.
import com.twosigma.flint.timeseries.TimeSeriesRDD
import scala.concurrent.duration._
val df = ... // A DataFrame whose rows have been sorted by their timestamps under "time" column
val tsRdd = TimeSeriesRDD.fromDF(dataFrame = df)(isSorted = true, timeUnit = MILLISECONDS)
One could also create a TimeSeriesRDD
from a RDD[Row]
or an OrderedRDD[Long, Row]
by providing a schema, e.g.
import com.twosigma.flint.timeseries._
import scala.concurrent.duration._
val rdd = ... // An RDD whose rows have sorted by their timestamps
val tsRdd = TimeSeriesRDD.fromRDD(
rdd,
schema = Schema("time" -> LongType, "price" -> DoubleType)
)(isSorted = true,
timeUnit = MILLISECONDS
)
It is also possible to create a TimeSeriesRDD
from a dataset stored as parquet format file(s). The TimeSeriesRDD.fromParquet()
function provides the option to specify which columns and/or the time range you are interested, e.g.
import com.twosigma.flint.timeseries._
import scala.concurrent.duration._
val tsRdd = TimeSeriesRDD.fromParquet(
sqlContext,
path = "hdfs://foo/bar/"
)(isSorted = true,
timeUnit = MILLISECONDS,
columns = Seq("time", "id", "price"), // By default, null for all columns
begin = "20100101", // By default, null for no boundary at begin
end = "20150101" // By default, null for no boundary at end
)
Similar to DataFrame
, one could get the schema
of a TimeSeriesRDD
, and perform operations like first()
, cache()
, collect()
, repartition()
, persist()
, etc. Other than those basic operations supported by DataFrame
or RDD
, one could manipulate rows and columns with the following functions.
cast
A function to cast numeric columns to a different numeric type (e.g. DoubleType to IntegerType).
val priceTSRdd = ... // A TimeSeriesRDD with schema Schema("time" -> LongType, "id", "price" -> DoubleType)
val result = priceTSRdd.cast("price" -> IntegerType)
keepRows
(ordeleteRows
) A function to filter rows based on given criteria.
val priceTSRdd = ... // A TimeSeriesRDD with columns "time", "id", and "price"
val result = priceTSRdd.keepRows { row: Row => row.getAs[Double]("price") > 100.0 }
deleteColumns
(orkeepColumns
) A function to filter columns by names.
val priceTSRdd = ... // A TimeSeriesRDD with columns "time", "id", and "price"
val result1 = priceTSRdd.keepColumns("time") // A TimeSeriesRDD with only "time" column
val result2 = priceTSRdd.deleteColumns("id") // A TimeSeriesRDD with only "time" and "price" columns
renameColumns
A function to modify column names without changing corresponding data types, e.g.
val priceTSRdd = ... // A TimeSeriesRDD with columns "time", "id", and "price"
val result = priceTSRdd.renameColumns("id" -> "ticker", "price" -> "highPrice")
setTime
A function to modify the time column, e.g.
val priceTSRdd = ... // A TimeSeriesRDD with columns "time", "id", and "price"
val result = priceTSRdd.setTime {
row: Row =>
// Set the new time to the closest trading time to the current time.
nextClosestTradingTime(row.get("id"), row.getAs[Long]("time"))
}
addColumns
A function to add to a row with one or more new columns whose values are calculated by using only values from a row. For example, we have aTimeSeriesRDD
with three columns "time", "highPrice", and "lowPrice", and we want to add a new column named "diff" to calculte the difference of the "highPrice" and "lowPrice".
val priceTSRdd = ... // A TimeSeriesRDD with columns "time", "highPrice", and "lowPrice"
val results = priceTSRdd.addColumns(
"diff" -> DoubleType -> {
r: Row => r.getAs[Double]("highPrice") - r.getAs[Double]("lowPrice")
}
)
// A TimeSeriesRDD with a new column "diff" = "highPrice" - "lowPrice"
addColumnsForCycle
A cycle is defined as a list of rows that share exactly the same timestamps. For a column to be added and a list of rows in a cycle, one could use this function to add the same or different values for each row in that cycle.
val priceTSRdd = ...
// A TimeSeriesRDD with columns "time", "id", and "sellingPrice"
// time id sellingPrice
// ----------------------
// 1000L 0 1.0
// 1000L 1 2.0
// 1000L 1 3.0
// 2000L 0 3.0
// 2000L 0 4.0
// 2000L 1 5.0
// 2000L 2 6.0
val results = priceTSRdd.addColumnsForCycle(
"adjustedSellingPrice" -> DoubleType -> { rows: Seq[Row] =>
rows.map { row => (row, row.getAs[Double]("sellingPrice") * rows.size) }.toMap
}
)
// time id sellingPrice adjustedSellingPrice
// -------------------------------------------
// 1000L 0 1.0 3.0
// 1000L 1 2.0 6.0
// 1000L 1 3.0 9.0
// 2000L 0 3.0 12.0
// 2000L 0 4.0 16.0
// 2000L 1 5.0 20.0
// 2000L 2 6.0 24.0
A group function is to group rows with nearby (or exactly the same) timestamps.
groupByCycle
A function to group rows within a cycle, i.e. rows with exactly the same timestamps. For example,
val priceTSRdd = ...
// A TimeSeriesRDD with columns "time" and "price"
// time price
// -----------
// 1000L 1.0
// 1000L 2.0
// 2000L 3.0
// 2000L 4.0
// 2000L 5.0
val results = priceTSRdd.groupByCycle()
// time rows
// ------------------------------------------------
// 1000L [[1000L, 1.0], [1000L, 2.0]]
// 2000L [[2000L, 3.0], [2000L, 4.0], [2000L, 5.0]]
groupByInterval
A funcion to group rows whose timestamps falling into an interval. Intervals could be defined by anotherTimeSeriesRDD
. Its timestamps will be used to defined intervals, i.e. two sequential timestamps define an interval. For example,
val priceTSRdd = ...
// A TimeSeriesRDD with columns "time" and "price"
// time price
// -----------
// 1000L 1.0
// 1500L 2.0
// 2000L 3.0
// 2500L 4.0
val clockTSRdd = ...
// A TimeSeriesRDD with only column "time"
// time
// -----
// 1000L
// 2000L
// 3000L
val results = priceTSRdd.groupByInterval(clockTSRdd)
// time rows
// ----------------------------------
// 1000L [[1000L, 1.0], [1500L, 2.0]]
// 2000L [[2000L, 3.0], [2500L, 4.0]]
addWindows
For each row, this function adds a new column whose value for a row is a list of rows within itswindow
.
val priceTSRdd = ...
// A TimeSeriesRDD with columns "time" and "price"
// time price
// -----------
// 1000L 1.0
// 1500L 2.0
// 2000L 3.0
// 2500L 4.0
val result = priceTSRdd.addWindows(Window.pastAbsoluteTime("1000ns"))
// time price window_past_1000ns
// ------------------------------------------------------
// 1000L 1.0 [[1000L, 1.0]]
// 1500L 2.0 [[1000L, 1.0], [1500L, 2.0]]
// 2000L 3.0 [[1000L, 1.0], [1500L, 2.0], [2000L, 3.0]]
// 2500L 4.0 [[1500L, 2.0], [2000L, 3.0], [2500L, 4.0]]
A temporal join function is a join function defined by a matching criteria over time. A tolerance
in temporal join matching criteria specifies how much it should look past or look futue.
leftJoin
A function performs the temporal left-join to the rightTimeSeriesRDD
, i.e. left-join using inexact timestamp matches. For each row in the left, append the most recent row from the right at or before the same time. An example to join twoTimeSeriesRDD
s is as follows.
val leftTSRdd = ...
val rightTSRdd = ...
val result = leftTSRdd.leftJoin(rightTSRdd, tolerance = "1day")
futureLeftJoin
A function performs the temporal future left-join to the rightTimeSeriesRDD
, i.e. left-join using inexact timestamp matches. For each row in the left, appends the closest future row from the right at or after the same time.
val result = leftTSRdd.futureLeftJoin(rightTSRdd, tolerance = "1day")
Summarize functions are the functions to apply summarizer(s) to rows within a certain period, like cycle, interval, windows, etc.
summarizeCycles
A function computes aggregate statistics of rows that are within a cycle, i.e. rows share a timestamp.
val volTSRdd = ...
// A TimeSeriesRDD with columns "time", "id", and "volume"
// time id volume
// ------------
// 1000L 1 100
// 1000L 2 200
// 2000L 1 300
// 2000L 2 400
val result = volTSRdd.summarizeCycles(Summary.sum("volume"))
// time volume_sum
// ----------------
// 1000L 300
// 2000L 700
Similarly, we could summarize over intervals, windows, or the whole time series data set. See
summarizeIntervals
summarizeWindows
addSummaryColumns
One could check timeseries.summarize.summarizer
for different kinds of summarizer(s), like ZScoreSummarizer
, CorrelationSummarizer
, NthCentralMomentSummarizer
etc.
flint.math.stat.regression
aims to provide a library similar to apache statistics package and python statsmodels package.
import breeze.linalg.DenseVector
import com.twosigma.flint.math.stat.regression._
// Generate a random data set from a linear model with beta = [1.0, 2.0] and intercept = 3.0
val data = WeightedLabeledPoint.generateSampleData(sc, DenseVector(1.0, 2.0), 3.0)
// Fit the data using the OLS linear regression.
val model = OLSMultipleLinearRegression.regression(data)
// Retrieve the estimate beta and intercept.
model.estimateRegressionParameters
The followiing table list different implementations cross different packages or libraries.
- flint.math.stat -
flint.math.stat.regression.LinearRegressionModel
- apache.commons.math3 -
apache.commons.math3.stat.regression.OLSMultipleLinearRegression
- statsmodels -
statsmodels.api
in Python
flint.stat | apache.commons.math3 | statsmodels |
---|---|---|
calculateCenteredTSS | n/a | centered_tss |
calculateHC0 | n/a | cov_HC0 |
calculateHC1 | n/a | cov_HC1 |
calculateEigenvaluesOfGramianMatrix | n/a | eigenvals |
calculateRegressionParametersPValue | n/a | pvalues |
calculateRegressionParametersTStat | n/a | tvalues |
calculateRSquared | n/a | rsquared |
calculateSumOfSquaredResidue | n/a | ssr |
calculateStandardErrorsOfHC0 | n/a | HC0_se |
calculateStandardErrorsOfHC1 | n/a | HC1_se |
calculateUncenteredTSS | n/a | uncentered_tss |
estimateErrorVariance | estimateErrorVariance | mse_resid |
estimateRegressionParameters | estimateRegressionParameters | params |
estimateRegressionParametersVariance | estimateRegressionParametersVariance | normalized_cov_params |
estimateRegressionParametersStandardErrors | estimateRegressionParametersStandardErrors | bse |
estimateErrorVariance | estimateErrorVariance | scale |
getN | getN | nobs |
In order to accept your code contributions, please fill out the appropriate Contributor License Agreement in the cla
folder and submit it to [email protected].
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