Welcome to the world of time series analysis with TSLib, the open-source library tailored for deep learning researchers. Whether you are interested in long-term forecasting, imputation, anomaly detection, or classification, this guide will walk you through the steps needed to effectively use TSLib for your projects.

Getting Started with TSLib

Before diving into the exciting capabilities of TSLib, make sure to follow the installation process and setup your environment.

Step 1: Installing TSLib

To get started, first install Python 3.8 if you haven’t already. The easiest way to install TSLib is by using pip. Execute the following command:

pip install -r requirements.txt

Step 2: Prepare Your Data

Next, you need to prepare your dataset. TSLib offers well pre-processed datasets readily available for downloading:

• Google Drive
• Baidu Drive

After downloading, place your data in the dataset folder.

Step 3: Train and Evaluate Your Model

TSLib comes with experiment scripts for all benchmarks found under the scripts folder. Here are some examples of how to run separate models for different tasks:

# Long-term Forecast
bash scripts/long_term_forecast/ETT_script_TimesNet_ETTh1.sh

# Short-term Forecast
bash scripts/short_term_forecast/TimesNet_M4.sh

# Imputation
bash scripts/imputation/ETT_script_TimesNet_ETTh1.sh

# Anomaly Detection
bash scripts/anomaly_detection/PSMTimesNet.sh

# Classification
bash scripts/classification/TimesNet.sh

Step 4: Develop Your Own Model

If you want to create a custom model, add your model file to the models folder and make sure to include it in the Exp_Basic.model_dict of exp_basic.py. Make sure to create the appropriate scripts under the scripts folder as well!

Understanding the Code Structure: An Analogy

Think of TSLib like a recipe book for deep time series models. Each recipe (or code) is tailored for a different dish:

• **Ingredients (Data)**: Just like cooking, you need the right ingredients (datasets) to prepare your dish (execute models).
• **Preparation Steps (Scripts)**: Following each recipe, you need to step through the preparation stages (training scripts) to create your dish.
• **Taste Testing (Evaluation)**: Finally, just as any cook would taste their dish and make adjustments, you evaluate your model performance on various tasks to ensure its effectiveness.

Troubleshooting Tips

If you encounter issues during installation, training, or model evaluation, here are some troubleshooting ideas:

• Ensure you are using Python 3.8 or above.
• Check that all required datasets are correctly placed in the dataset folder.
• If a script fails to run, confirm the file paths are correct and scripts are executable.
• Consult the documentation or GitHub issues for specific error messages.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.