RemixAutoML (version 0.5.4)

AutoXGBoostCARMA: AutoXGBoostCARMA

Description

AutoXGBoostCARMA Mutlivariate Forecasting with calendar variables, Holiday counts, holiday lags, holiday moving averages, differencing, transformations, interaction-based categorical encoding using target variable and features to generate various time-based aggregated lags, moving averages, moving standard deviations, moving skewness, moving kurtosis, moving quantiles, parallelized interaction-based fourier pairs by grouping variables, and Trend Variables.

Usage

AutoXGBoostCARMA(
  data = NULL,
  XREGS = NULL,
  NonNegativePred = FALSE,
  RoundPreds = FALSE,
  TrainOnFull = FALSE,
  TargetColumnName = NULL,
  DateColumnName = NULL,
  HierarchGroups = NULL,
  GroupVariables = NULL,
  FC_Periods = 5,
  SaveDataPath = NULL,
  PDFOutputPath = NULL,
  TimeUnit = "week",
  TimeGroups = c("weeks", "months"),
  TargetTransformation = FALSE,
  Methods = c("Asinh", "Log", "LogPlus1", "Sqrt"),
  EncodingMethod = "binary",
  AnomalyDetection = NULL,
  Lags = c(1:5),
  MA_Periods = c(1:5),
  SD_Periods = NULL,
  Skew_Periods = NULL,
  Kurt_Periods = NULL,
  Quantile_Periods = NULL,
  Quantiles_Selected = NULL,
  Difference = TRUE,
  FourierTerms = 0,
  CalendarVariables = c("second", "minute", "hour", "wday", "mday", "yday", "week",
    "wom", "isoweek", "month", "quarter", "year"),
  HolidayVariable = c("USPublicHolidays", "EasterGroup", "ChristmasGroup",
    "OtherEcclesticalFeasts"),
  HolidayLookback = NULL,
  HolidayLags = 1L,
  HolidayMovingAverages = 3L,
  TimeTrendVariable = FALSE,
  DataTruncate = FALSE,
  ZeroPadSeries = NULL,
  SplitRatios = c(1 - 10/100, 10/100),
  TreeMethod = "hist",
  NThreads = max(1, parallel::detectCores() - 2L),
  PartitionType = "random",
  Timer = TRUE,
  DebugMode = FALSE,
  EvalMetric = "MAE",
  LossFunction = "reg:squarederror",
  GridTune = FALSE,
  GridEvalMetric = "mae",
  ModelCount = 30L,
  MaxRunsWithoutNewWinner = 20L,
  MaxRunMinutes = 24L * 60L,
  NTrees = 1000L,
  LearningRate = 0.3,
  MaxDepth = 9L,
  MinChildWeight = 1,
  SubSample = 1,
  ColSampleByTree = 1
)

Arguments

data

Supply your full series data set here

XREGS

Additional data to use for model development and forecasting. Data needs to be a complete series which means both the historical and forward looking values over the specified forecast window needs to be supplied.

NonNegativePred

TRUE or FALSE

RoundPreds

Rounding predictions to an integer value. TRUE or FALSE. Defaults to FALSE

TrainOnFull

Set to TRUE to train on full data

TargetColumnName

List the column name of your target variables column. E.g. 'Target'

DateColumnName

List the column name of your date column. E.g. 'DateTime'

HierarchGroups

= NULL Character vector or NULL with names of the columns that form the interaction hierarchy

GroupVariables

Defaults to NULL. Use NULL when you have a single series. Add in GroupVariables when you have a series for every level of a group or multiple groups.

FC_Periods

Set the number of periods you want to have forecasts for. E.g. 52 for weekly data to forecast a year ahead

SaveDataPath

Path to save modeling data

PDFOutputPath

Supply a path to save model insights to PDF

TimeUnit

List the time unit your data is aggregated by. E.g. '1min', '5min', '10min', '15min', '30min', 'hour', 'day', 'week', 'month', 'quarter', 'year'

TimeGroups

Select time aggregations for adding various time aggregated GDL features.

TargetTransformation

Run AutoTransformationCreate() to find best transformation for the target variable. Tests YeoJohnson, BoxCox, and Asigh (also Asin and Logit for proportion target variables).

Methods

Choose from 'YeoJohnson', 'BoxCox', 'Asinh', 'Log', 'LogPlus1', 'Sqrt', 'Asin', or 'Logit'. If more than one is selected, the one with the best normalization pearson statistic will be used. Identity is automatically selected and compared.

EncodingMethod

Choose from 'binary', 'm_estimator', 'credibility', 'woe', 'target_encoding', 'poly_encode', 'backward_difference', 'helmert'

AnomalyDetection

NULL for not using the service. Other, provide a list, e.g. AnomalyDetection = list('tstat_high' = 4, tstat_low = -4)

Lags

Select the periods for all lag variables you want to create. E.g. c(1:5,52) or list('day' = c(1:10), 'weeks' = c(1:4))

MA_Periods

Select the periods for all moving average variables you want to create. E.g. c(1:5,52) or list('day' = c(2:10), 'weeks' = c(2:4))

SD_Periods

Select the periods for all moving standard deviation variables you want to create. E.g. c(1:5,52) or list('day' = c(2:10), 'weeks' = c(2:4))

Skew_Periods

Select the periods for all moving skewness variables you want to create. E.g. c(1:5,52) or list('day' = c(2:10), 'weeks' = c(2:4))

Kurt_Periods

Select the periods for all moving kurtosis variables you want to create. E.g. c(1:5,52) or list('day' = c(2:10), 'weeks' = c(2:4))

Quantile_Periods

Select the periods for all moving quantiles variables you want to create. E.g. c(1:5,52) or list('day' = c(2:10), 'weeks' = c(2:4))

Quantiles_Selected

Select from the following c('q5','q10','q15','q20','q25','q30','q35','q40','q45','q50','q55','q60','q65','q70','q75','q80','q85','q90','q95')

Difference

Set to TRUE to put the I in ARIMA

FourierTerms

Set to the max number of pairs

CalendarVariables

NULL, or select from 'second', 'minute', 'hour', 'wday', 'mday', 'yday', 'week', 'wom', 'isoweek', 'month', 'quarter', 'year'

HolidayVariable

NULL, or select from 'USPublicHolidays', 'EasterGroup', 'ChristmasGroup', 'OtherEcclesticalFeasts'

HolidayLookback

Number of days in range to compute number of holidays from a given date in the data. If NULL, the number of days are computed for you.

HolidayLags

Number of lags for the holiday counts

HolidayMovingAverages

Number of moving averages for holiday counts

TimeTrendVariable

Set to TRUE to have a time trend variable added to the model. Time trend is numeric variable indicating the numeric value of each record in the time series (by group). Time trend starts at 1 for the earliest point in time and increments by one for each success time point.

DataTruncate

Set to TRUE to remove records with missing values from the lags and moving average features created

ZeroPadSeries

NULL to do nothing. Otherwise, set to 'maxmax', 'minmax', 'maxmin', 'minmin'. See TimeSeriesFill for explanations of each type

SplitRatios

E.g c(0.7,0.2,0.1) for train, validation, and test sets

TreeMethod

Choose from 'hist', 'gpu_hist'

NThreads

Set the maximum number of threads you'd like to dedicate to the model run. E.g. 8

PartitionType

Select 'random' for random data partitioning 'time' for partitioning by time frames

Timer

Setting to TRUE prints out the forecast number while it is building

DebugMode

Setting to TRUE generates printout of all header code comments during run time of function

EvalMetric

Select from 'r2', 'RMSE', 'MSE', 'MAE'

LossFunction

Default is 'reg:squarederror'. Other options include 'reg:squaredlogerror', 'reg:pseudohubererror', 'count:poisson', 'survival:cox', 'survival:aft', 'aft_loss_distribution', 'reg:gamma', 'reg:tweedie'

GridTune

Set to TRUE to run a grid tune

GridEvalMetric

This is the metric used to find the threshold 'poisson', 'mae', 'mape', 'mse', 'msle', 'kl', 'cs', 'r2'

ModelCount

Set the number of models to try in the grid tune

MaxRunsWithoutNewWinner

Number of consecutive runs without a new winner in order to terminate procedure

MaxRunMinutes

Default 24L*60L

NTrees

Select the number of trees you want to have built to train the model

LearningRate

Learning Rate

MaxDepth

Depth

MinChildWeight

Records in leaf

SubSample

Random forecast setting

ColSampleByTree

Self explanatory

Value

See examples

See Also

Other Automated Panel Data Forecasting: AutoCatBoostCARMA(), AutoCatBoostHurdleCARMA(), AutoCatBoostVectorCARMA(), AutoH2OCARMA(), AutoLightGBMCARMA()

Examples

# NOT RUN {
# Load data
data <- data.table::fread('https://www.dropbox.com/s/2str3ek4f4cheqi/walmart_train.csv?dl=1')

# Ensure series have no missing dates (also remove series with more than 25% missing values)
data <- RemixAutoML::TimeSeriesFill(
  data,
  DateColumnName = 'Date',
  GroupVariables = c('Store','Dept'),
  TimeUnit = 'weeks',
  FillType = 'maxmax',
  MaxMissingPercent = 0.25,
  SimpleImpute = TRUE)

# Set negative numbers to 0
data <- data[, Weekly_Sales := data.table::fifelse(Weekly_Sales < 0, 0, Weekly_Sales)]

# Remove IsHoliday column
data[, IsHoliday := NULL]

# Create xregs (this is the include the categorical variables instead of utilizing only the interaction of them)
xregs <- data[, .SD, .SDcols = c('Date', 'Store', 'Dept')]

# Change data types
data[, ':=' (Store = as.character(Store), Dept = as.character(Dept))]
xregs[, ':=' (Store = as.character(Store), Dept = as.character(Dept))]

 # Build forecast
XGBoostResults <- AutoXGBoostCARMA(

  # Data Artifacts
  data = data,
  NonNegativePred = FALSE,
  RoundPreds = FALSE,
  TargetColumnName = 'Weekly_Sales',
  DateColumnName = 'Date',
  HierarchGroups = NULL,
  GroupVariables = c('Store','Dept'),
  TimeUnit = 'weeks',
  TimeGroups = c('weeks','months'),

  # Data Wrangling Features
  EncodingMethod = 'binary',
  ZeroPadSeries = NULL,
  DataTruncate = FALSE,
  SplitRatios = c(1 - 10 / 138, 10 / 138),
  PartitionType = 'timeseries',
  AnomalyDetection = NULL,

  # Productionize
  FC_Periods = 0,
  TrainOnFull = FALSE,
  NThreads = 8,
  Timer = TRUE,
  DebugMode = FALSE,
  SaveDataPath = NULL,
  PDFOutputPath = NULL,

  # Target Transformations
  TargetTransformation = TRUE,
  Methods = c('BoxCox', 'Asinh', 'Asin', 'Log',
              'LogPlus1', 'Sqrt', 'Logit','YeoJohnson'),
  Difference = FALSE,

  # Features
  Lags = list('weeks' = seq(1L, 10L, 1L),
              'months' = seq(1L, 5L, 1L)),
  MA_Periods = list('weeks' = seq(5L, 20L, 5L),
                    'months' = seq(2L, 10L, 2L)),
  SD_Periods = NULL,
  Skew_Periods = NULL,
  Kurt_Periods = NULL,
  Quantile_Periods = NULL,
  Quantiles_Selected = c('q5','q95'),
  XREGS = xregs,
  FourierTerms = 4,
  CalendarVariables = c('week', 'wom', 'month', 'quarter'),
  HolidayVariable = c('USPublicHolidays','EasterGroup',
    'ChristmasGroup','OtherEcclesticalFeasts'),
  HolidayLookback = NULL,
  HolidayLags = 1,
  HolidayMovingAverages = 1:2,
  TimeTrendVariable = TRUE,

  # ML eval args
  TreeMethod = 'hist',
  EvalMetric = 'RMSE',
  LossFunction = 'reg:squarederror',

  # ML grid tuning
  GridTune = FALSE,
  ModelCount = 5,
  MaxRunsWithoutNewWinner = 20L,
  MaxRunMinutes = 24L*60L,

  # ML args
  NTrees = 300,
  LearningRate = 0.3,
  MaxDepth = 9L,
  MinChildWeight = 1.0,
  SubSample = 1.0,
  ColSampleByTree = 1.0)

UpdateMetrics <- print(
  XGBoostResults$ModelInformation$EvaluationMetrics[
    Metric == 'MSE', MetricValue := sqrt(MetricValue)])
print(UpdateMetrics)
XGBoostResults$ModelInformation$EvaluationMetricsByGroup[order(-R2_Metric)]
XGBoostResults$ModelInformation$EvaluationMetricsByGroup[order(MAE_Metric)]
XGBoostResults$ModelInformation$EvaluationMetricsByGroup[order(MSE_Metric)]
XGBoostResults$ModelInformation$EvaluationMetricsByGroup[order(MAPE_Metric)]
# }