Practical overview
When it fits
Use it when you need repository code and scripts for long-horizon time-series forecasting experiments, or a linear-model baseline to compare with other forecasting architectures.
Who it is for
- Researchers comparing long-horizon forecasting methods
- Practitioners seeking a PyTorch linear-model baseline for univariate or multivariate time series
- Users studying the effects of look-back windows or model variants
Documented uses
- Train Linear, NLinear or DLinear on a long-term forecasting dataset
- Compare forecasting approaches using the included benchmark
- Explore univariate and multivariate forecasting experiments
- Inspect model weights after training
Requirements & limits
The repository is public and identifies an Apache-2.0 license. The README documents local setup with Conda and Python 3.6.9.
- Linear is a single-layer model; NLinear normalizes the input around its last value; DLinear separates trend and seasonal components.
- The README describes scripts for long-term forecasting, look-back-window studies and embedding experiments.
- Benchmark datasets are linked through Google Drive and are intended to be placed in the repository's dataset directory.
- The repository also includes several Transformer implementations for paper-related comparisons.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the forecasting environment
Create the documented Conda environment and prepare the local dataset directory.
Before you start
- Conda installed
- A local copy of the Ltsf Linear repository
Steps
Create a Conda environment with Python 3.6.9 using the README's environment command.
Activate the LTSF_Linear environment.
Install dependencies from requirements.txt.
Create a dataset directory at the repository root and place prepared benchmark data there.
Expected outcome
The documented Python environment and dataset location are prepared for the repository's forecasting experiments.
Guide limitations
- This guide covers setup and data placement, not running or validating an experiment.
- The README points to nine prepared benchmark datasets hosted through Google Drive.
Train a model on the Exchange-Rate example
Use the repository's documented script to start long-term forecasting training on the Exchange-Rate dataset.
Before you start
- The Conda environment and dependencies described in the README
- The Exchange-Rate dataset placed in the local dataset directory
Steps
Open scripts/EXP-LongForecasting/Linear/exchange_rate.sh.
Run the script with sh from the repository.
To select another supported LTSF-Linear model, change the model name in the script to Linear, DLinear or NLinear.
Expected outcome
The script starts training; the README says results are written under logs/LongForecasting.
Guide limitations
- The example uses DLinear by default.
- The README gives a typical duration of 5–20 minutes for each experiment; actual runtime may vary.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- cure-lab/LTSF-Linear · Official homepage title · retained in its source language