SQL-Server-R-Services-Samples/RetailForecasting/SQLR/time_series_forecasting_train_obsolate.sql at master · Roboticmechart22/SQL-Server-R-Services-Samples · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
SET ANSI_NULLS ON
GO

/****** Create the Stored Procedure for Retail Forecasting Template ******/

SET QUOTED_IDENTIFIER ON
GO

DROP PROCEDURE IF EXISTS time_series_forecasting
GO

-- =============================================
-- Author:		<Author,,Name>
-- Create date: <Create Date,,>
-- Description:	<Description,,>
-- =============================================
CREATE PROCEDURE [time_series_forecasting] @testlength int,
					   @modelname varchar(10),
				           @connectionString varchar(300)
AS
BEGIN

  DECLARE @inquery NVARCHAR(max) = N'SELECT * FROM forecasting'

  EXEC sp_execute_external_script @language = N'R',
                                  @script = N'
library(forecast)
library(plyr)
####################################################################################################
## Compute context
####################################################################################################
sql_share_directory <- paste("c:\\AllShare\\", Sys.getenv("USERNAME"), sep = "")
dir.create(sql_share_directory, recursive = TRUE)
sql <- RxInSqlServer(connectionString = connection_string,
                     shareDir = sql_share_directory)
local <- RxLocalSeq()
####################################################################################################
## Modeling parameters
####################################################################################################
seasonality <- 52
observation.freq <- "week"
timeformat <- "%m/%d/%Y"
####################################################################################################
## Time Series Model
##   1: Seasonal trend decomposition (STL) +  exponential smoothing (ETS)
##   2: Seasonal naive
##   3: Seasonal trend decomposition (STL) + ARIMA
####################################################################################################
# Helper functions extracting date-related information
weeknum <- function(date) {
  date <- as.Date(date)
  as.numeric(format(date, "%U"))
}
year <- function(date) {
  date <- as.Date(date)
  as.numeric(format(date, "%Y"))
}
date.info <- function(df) {
  date <- df$time[1]
  c(year(date), weeknum(date))
}
if (testlength == 0) {
	horizon <- seasonality
	train <- data
	min_time <- as.Date(max(train$time)) + 7
	test_ts <- seq(from = min_time, by = observation.freq, length.out = seasonality)
} else {
	horizon <- testlength
    train.length <- data.length - horizon
	train <- data[1:train.length, ]
	test <- data[(train.length+1):data.length, ]
	test_ts <- test$time
}

data_input <- InputDataSet

# Forecasting Function
forecast_func <- function(data, model.name) {

  if (testlength == 0) {
	horizon <- seasonality
	train <- data
	min_time <- as.Date(max(train$time)) + 7
	test_ts <- seq(from = min_time, by = observation.freq, length.out = seasonality)
  } else {
	horizon <- testlength
    train.length <- data.length - horizon
	train <- data[1:train.length, ]
	test <- data[(train.length+1):data.length, ]
	test_ts <- test$time
  }
  # Missing data: replace na with average
  train$value[is.na(train$value)] <- mean(train$value, na.rm = TRUE)

  # Build forecasting models
  train.ts <- ts(train$value, frequency = seasonality, start = date.info(train))
  if (model.name == "ets") {
    train.stl <- stl(train.ts, s.window="periodic")
    train.model <- forecast(train.stl, h = horizon, method = 'ets', ic = 'bic', opt.crit='mae')
  } else if (model.name == "snaive") {
    train.model <- snaive(train.ts, h = horizon)
  } else {
    train.model <- stlf(train.ts, h = horizon, method = "arima", s.window = "periodic")
  }

  forecast.value <- train.model$mean
  forecast.lo95 <- train.model$lower[,1]
  forecast.hi95 <- train.model$upper[,1]

  output <- data.frame(time = test_ts, cbind(forecast.value, forecast.lo95, forecast.hi95))
  colnames(output)[-1] <- paste(c("forecast", "lo95", "hi95"), model.name, sep = ".")

  return(output)
}
data_output <- ddply(data_input, .(ID1, ID2), forecast_func, modelname)

forecast_table <- RxSqlServerData(table = paste("forecasts", modelname, sep = "_"),
                                  connectionString = connection_string)

rxDataStep(inData = data_output,
           outFile = forecast_table,
           overwrite = TRUE)'
, @input_data_1 = @inquery
, @params = N'@testlength int, @@modelname varchar(10), connection_string varchar(300)'
, @testlength = @testlength
, @modelname = @modelname
, @connection_string = @connectionString
END
;
GO