https://github.com/andrewdarnall/liquidsky
A benchmarking of univariate time series forecasting with statisticsal models and deep learning models
https://github.com/andrewdarnall/liquidsky
arima data-analytics liquid-network lstm time-series-forecasting transformer
Last synced: about 1 year ago
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A benchmarking of univariate time series forecasting with statisticsal models and deep learning models
- Host: GitHub
- URL: https://github.com/andrewdarnall/liquidsky
- Owner: AndrewDarnall
- Created: 2024-10-10T12:17:32.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-12T19:35:51.000Z (about 1 year ago)
- Last Synced: 2025-06-12T20:53:28.981Z (about 1 year ago)
- Topics: arima, data-analytics, liquid-network, lstm, time-series-forecasting, transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 7.54 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Liquid Sky - A Time Series Forecasting Benchmark
---
This project explores the benchmarking of **univariate time series forecasting**, focusing on comparing the performances of various models. The models analyzed include traditional
**statistical models** such as **ARIMA** and **SARIMA**, as well as modern **deep learning models** like **LSTM** and **Transformer** networks.
Additionally, the study delves into a new class of emerging technologies, **continuous deep learning models**,
with a particular emphasis on **Liquid Time Constant Networks** and their **Closed-form Approximation**.
The benchmark is conducted using a simple univariate time series dataset, serving as a reference for all models.
Beyond evaluating the **inference performance** of each model, the analysis also considers the **advantages and trade-offs**
of using one class of models over others for similar forecasting tasks.
This comprehensive approach provides insights into the conditions under which certain models may outperform others,
facilitating more informed decision-making in model selection for time series forecasting.
------
# Requirements
| Component | Version |
|-----------|---------|
| Python | `3.12.5`|
| pip | `22.0.4`|
# Set Up the Python Environment
```bash
python -m venv data-analytics
```
# Download the Dependencies
```bash
python -m pip install -r requirements.txt
```
------
# Download the Datasets and Run the notebook
Clone the GitHub repo and change into the repo's directory:
```bash
git clone https://github.com/AndrewDarnall/TSF-Benchmark.git
cd TSF-Benchmark
```
Create the datasets directory:
```bash
mkdir datasets
cd datasets
```
The following notebook uses the [AirPassengers](https://www.kaggle.com/datasets/rakannimer/air-passengers) dataset, which once downloaded, extract the .zip file into the `datasets/` directory
```bash
mv ~/Downloads/archive.zip .
unzip archive.zip
rm archive.zip
cd ..
```
From the commandline terminal (in Linux and macOS) run the following command:
```bash
jupyter-notebook ./TSF-Benchmark.ipynb --port=8086
```