AutoXGBoostCARMA: AutoXGBoostCARMA

In AdrianAntico/ModelingTools: AutoQuant

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,
  TimeWeights = NULL,
  NonNegativePred = FALSE,
  RoundPreds = FALSE,
  TrainOnFull = FALSE,
  TargetColumnName = NULL,
  DateColumnName = NULL,
  HierarchGroups = NULL,
  GroupVariables = NULL,
  FC_Periods = 1,
  SaveDataPath = NULL,
  TimeUnit = NULL,
  TimeGroups = NULL,
  TargetTransformation = FALSE,
  Methods = c("Asinh", "Log", "LogPlus1", "Sqrt"),
  EncodingMethod = "target_encoding",
  AnomalyDetection = NULL,
  Lags = NULL,
  MA_Periods = NULL,
  SD_Periods = NULL,
  Skew_Periods = NULL,
  Kurt_Periods = NULL,
  Quantile_Periods = NULL,
  Quantiles_Selected = c("q5", "q95"),
  Difference = FALSE,
  FourierTerms = 0,
  CalendarVariables = NULL,
  HolidayVariable = NULL,
  HolidayLookback = NULL,
  HolidayLags = NULL,
  HolidayMovingAverages = NULL,
  TimeTrendVariable = FALSE,
  DataTruncate = FALSE,
  ZeroPadSeries = NULL,
  SplitRatios = c(0.95, 0.05),
  PartitionType = "random",
  TreeMethod = "hist",
  NThreads = max(1, parallel::detectCores() - 2L),
  Timer = TRUE,
  DebugMode = FALSE,
  EvalMetric = "MAE",
  LossFunction = "reg:squarederror",
  GridTune = FALSE,
  GridEvalMetric = "mae",
  ModelCount = 30L,
  MaxRunsWithoutNewWinner = 20L,
  MaxRunMinutes = 24L * 60L,
  EarlyStoppingRounds = 100L,
  NTrees = 500L,
  num_parallel_tree = 1,
  LearningRate = 0.5,
  MaxDepth = 6L,
  MinChildWeight = 1,
  SubSample = 0.7,
  ColSampleByTree = 1,
  alpha = 0.1,
  lambda = 0.9,
  SaveModel = FALSE,
  ArgsList = NULL,
  ModelID = "FC001",
  TVT = NULL
)

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.
TimeWeights	= NULL
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
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
PartitionType	Select 'random' for random data partitioning 'time' for partitioning by time frames
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
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
alpha	0. L1 Reg.
lambda	1. L2 Reg.
SaveModel	Logical. If TRUE, output ArgsList will have a named element 'Model' with the CatBoost model object
ArgsList	ArgsList is for scoring. Must contain named element 'Model' with a catboost model object
ModelID	Something to name your model if you want it saved
TVT	Passthrough

Value

See examples

Author(s)

Adrian Antico

See Also

Other Automated Panel Data Forecasting: AutoCatBoostCARMA(), 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 <- AutoQuant::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,

  # 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)]

## End(Not run)

AdrianAntico/ModelingTools documentation built on Feb. 1, 2024, 7:33 a.m.