Time Series Prediction with Integrated Tuning
Adjust ts_data
Fit Time Series Model
Predict Time Series Model
MSE
R2
Select Optimal Hyperparameters for Time Series Models
sMAPE
Subset Extraction for Time Series Data
ARIMA
Augmentation by Awareness
Augmentation by Awareness Smooth
Augmentation by Flip
Augmentation by Jitter
No Augmentation
Augmentation by Shrink
Augmentation by Stretch
Augmentation by Wormhole
ts_data
ELM
Exponential Moving Average (EMA)
EMD Filter
FFT Filter
Hodrick-Prescott Filter
Kalman Filter
LOWESS Smoothing
Moving Average (MA)
No Filter
Quadratic Exponential Smoothing
Recursive Filter
Robust EMD Filter
Seasonal Adjustment
Simple Exponential Smoothing
Time Series Smooth
Smoothing Splines
Wavelet Filter
Winsorization of Time Series
Extract the First Observations from a ts_data Object
Time Series Integrated Tune
KNN Time Series Prediction
MLP
Adaptive Normalization
First Differences
Adaptive Normalization with EMA
Global Min–Max Normalization
No Normalization
Sliding-Window Min–Max Normalization
Time Series Projection
TSReg
TSRegSW
Random Forest
Time Series Sample
SVM
Time Series Tune
Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.
Useful links