diff --git a/DESCRIPTION b/DESCRIPTION
index 612fe224..2f4a1e22 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: finnts
 Title: Microsoft Finance Time Series Forecasting Framework
-Version: 0.4.0.9002
+Version: 0.4.0.9003
 Authors@R: 
     c(person(given = "Mike",
            family = "Tokic",
diff --git a/R/prep_data.R b/R/prep_data.R
index 14419b93..847492b9 100644
--- a/R/prep_data.R
+++ b/R/prep_data.R
@@ -8,7 +8,7 @@
 #' @param combo_variables List of column headers within input data to be used to separate individual time series.
 #' @param target_variable The column header formatted as a character value within input data you want to forecast.
 #' @param date_type The date granularity of the input data. Finn accepts the following as a character string:
-#'   day, week, month, quarter, year.
+#'   day, week, month, quarter, year. Default will detect automatically based on the difference of the first two dates.
 #' @param forecast_horizon Number of periods to forecast into the future.
 #' @param external_regressors List of column headers within input data to be used as features in multivariate models.
 #' @param hist_start_date Date value of when your input_data starts. Default of NULL uses earliest date value in
diff --git a/R/prep_models.R b/R/prep_models.R
index 78e1e349..6e807588 100644
--- a/R/prep_models.R
+++ b/R/prep_models.R
@@ -802,6 +802,35 @@ model_hyperparameters <- function(run_info,
   )
 }
 
+#' Detect date type
+#'
+#' @param dates Dates vector. Will use first two values to calculate difference.
+#'
+#' @return Returns date_type
+#' @noRd
+get_date_type <- function(dates, ...) {
+  dates <- as.Date(dates, origin = "1970-01-01", ...)
+  dayInterval <- as.integer(difftime(
+    dates[2],
+    dates[1],
+    units = "days"
+  ))
+  date_type <- if (dayInterval == 1) {
+    "day"
+  } else if (dayInterval == 7) {
+    "week"
+  } else if (dayInterval %in% 28:31) {
+    "month"
+  } else if (round(dayInterval/4) %in% 28:31) {
+    "quarter"
+  } else if (dayInterval %in% 365:366) {
+    "year"
+  } else {
+    stop("Dates data has to be daily, weekly, quarterly, monthly, or yearly")
+  }
+  return(date_type)
+}
+
 #' Gets the right frequency numbers
 #'
 #' @param date_type year, quarter, month, week, day
diff --git a/man/prep_data.Rd b/man/prep_data.Rd
index c0e0dd71..0646e63a 100644
--- a/man/prep_data.Rd
+++ b/man/prep_data.Rd
@@ -41,7 +41,7 @@ Can also include external regressors for both historical and future data.}
 \item{target_variable}{The column header formatted as a character value within input data you want to forecast.}
 
 \item{date_type}{The date granularity of the input data. Finn accepts the following as a character string:
-day, week, month, quarter, year.}
+day, week, month, quarter, year. Default will detect automatically based on the difference of the first two dates.}
 
 \item{forecast_horizon}{Number of periods to forecast into the future.}
 
diff --git a/tests/testthat/test-date_type.R b/tests/testthat/test-date_type.R
new file mode 100644
index 00000000..e6b748b3
--- /dev/null
+++ b/tests/testthat/test-date_type.R
@@ -0,0 +1,10 @@
+# Check get_date_type() with different datasets
+
+test_that("get_date_type() detects date types correctly", {
+  # Using m750 dataset
+  expect_equal("month", get_date_type(modeltime::m750$date))
+  # Passing dates as integer values
+  expect_equal("day", get_date_type((Sys.Date()-1):Sys.Date()))
+  # Passing dates as Date values
+  expect_equal("day", get_date_type(as.Date((Sys.Date()-1):Sys.Date())))
+})