atspR — Getting Started

Automated Time-Series Preprocessing in R

v1.0.0 · MIT License · EasternTechFusion/atspR

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Pipeline

Raw data
   │
   ├─ (Stage 1) combine_datetime()      Merge date + time columns → POSIXct (optional)
   ├─ (Stage 2) fill_time_gaps()        Insert placeholder rows for missing timestamps
   │
   └─ ts_preprocess()                   ────────────────── 8 steps ──────────────────────────────
   │    (Stage 3) standardize_na()      Convert sentinel values to NA
   │    (Stage 4) coerce_numeric()      Parse character columns to numeric
   │    (Stage 5) split_data()          Temporal train/test split
   │    (Stage 6) missing_analysis()    Summarise NAs; decide DROP or IMPUTE
   │    (Stage 7) handle_missing()      Drop rows or interpolate (linear/KNN)
   │    (Stage 8) visualize_data()      Scatter plots per variable vs time
   │    (Stage 9) scale_data()          MinMax / Z-score / Robust (auto)
   │    (Stage 10) cross_validate()     Walk-forward k-fold CV (optional) — choose one : 
   |                                    Linear Regression (lm)/ Generalized Additive Model (gam)/
   │                                    Random Forest (rf)/ Decision Tree (dt)
   │
   └─ ts_export()                       Write all outputs to CSV

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Installation

# install.packages("devtools")
devtools::install_github("EasternTechFusion/atspR")
library(atspR)

Quick Start

Note: Column names must not contain parentheses ( ) or the package will fail to parse them. For example, rename WaterLevel() to WaterLevel before passing the data to atspR functions.

Univariate

library(atspR)

df$datetime <- as.POSIXct(df$datetime, 
                          format = "%Y-%m-%d %H:%M:%S")

gap <- fill_time_gaps(df,
                      time_col = "DateTime",
                      n        = 10,
                      unit     = "min")

result <- ts_preprocess(data          = gap$data,
                        train_ratio   = 0.8,
                        impute_method = "linear",
                        lags          = 24,
                        model_type    = "lm",
                        target_col    = "WaterLevel",
                        k_folds       = 5)

Multivariate

Case 1: Date + Time in separate columns

library(atspR)

df <- combine_datetime(my_data,
                       date_col  = "Date",
                       time_col  = "Time",
                       new_col   = "datetime",
                       time_type = "string")

gap <- fill_time_gaps(df,
                      time_col = "datetime",
                      n        = 1,
                      unit     = "hour")

result <- ts_preprocess(data        = gap$data,
                        train_ratio = 0.8,
                        target_col  = "VPD",
                        k_folds     = 5)

Case 2: Datetime in one column

library(atspR)

df$datetime <- as.POSIXct(df$datetime, format = "%Y-%m-%d %H:%M:%S")

gap <- fill_time_gaps(df,
                      time_col = "datetime",
                      n        = 1,
                      unit     = "hour")

result <- ts_preprocess(data        = gap$data,
                        train_ratio = 0.8,
                        target_col  = "VPD",
                        k_folds     = 5)

Case 3: Date only (daily data)

library(atspR)

gap <- fill_time_gaps(df,
                      time_col = "date",
                      n        = 1,
                      unit     = "day")

result <- ts_preprocess(data        = gap$data,
                        train_ratio = 0.8,
                        target_col  = "VPD",
                        k_folds     = 5)

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ts_preprocess() Parameters

Parameter	Type	Default	Description
data	data.frame	—	Input dataframe (after fill_time_gaps())
target_col	character	NULL	Response column for CV; NULL skips CV
train_ratio	numeric [0–1]	0.8	Proportion of data used for training
k_folds	integer	5	Number of walk-forward CV folds
impute_method	character	"linear"	"linear" or "knn"
scale_method	character	"auto"	"minmax", "zscore", "robust", or "auto"

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Export Results

ts_export(result, dir = "C:/Users/user/", prefix = "data")

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Cross-Validation

atspR uses walk-forward validation — each fold is preceded only by past data, so no future information leaks into training.

Seed(20%)  Fold1  Fold2  Fold3  Fold4  Fold5
[─────────][─────][─────][─────][─────][─────]

Fold 1: train = seed              → val = fold1
Fold 2: train = seed + fold1      → val = fold2
Fold 3: train = seed + fold1-2    → val = fold3
Fold 4: train = seed + fold1–3    → val = fold4
Fold 5: train = seed + fold1–4    → val = fold5

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Citation

Sueppong Mueanchamnong and Pattharaporn Thongnim (2026).
atspR: Automated Time-Series Preprocessing in R.
R package version 1.1.0.
https://github.com/EasternTechFusion/atspR

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