Discover, compare, and experiment with the latest forecasting models. Learn time series analysis with interactive tools, AI guidance, and real-world datasets.
Get personalized model recommendations and troubleshooting tips from our built-in AI assistant.
Experiment with real or synthetic data, tune model parameters, and visualize results instantly.
Explore detailed pages for classical, machine learning, deep learning, and hybrid models.
Access a curated collection of public time series datasets for benchmarking and learning.
A structured resource for mastering time series analysis, now enhanced with AI-powered tools and interactive learning experiences.
Whether you're a student, a data scientist, or a researcher, our AI-enhanced model library has the information and tools you need to succeed in your forecasting projects.
Learn the fundamentals of time series analysis and forecasting with beginner-friendly guides and infographics.
Try different models, tune parameters, and visualize results interactively with your own or sample data.
Use detailed comparison tables to select the best model for your needs and data characteristics.
Download code, datasets, and resources to apply forecasting in your own projects and research.
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Time series forecasting is the process of using historical data to predict future values. It’s widely used in finance, weather, energy, and more.
No! You can use the simulator and explore models without writing any code. For advanced users, Python code examples are provided.
Yes, the simulator allows you to upload your own CSV files or enter data manually for custom forecasting.
Use the AI Recommender or the comparison tables to get personalized suggestions based on your data and goals.
Check the Concepts page, ask the AI Assistant, or contact us using the form below.
These metrics help you judge how well a model predicts future values. Lower MAE, RMSE, and MAPE mean better accuracy. R² (closer to 1) means the model explains more of the data’s variation. See the Concepts page for detailed explanations and examples.
Yes, some models (like VAR, SARIMAX, LSTM, Transformer, and others) support multivariate forecasting. Check each model’s page for details and examples.
AI/ML models can be powerful but may require lots of data, careful tuning, and may not always be interpretable. They can overfit, struggle with rare events, or fail if the data distribution changes. Always validate results and compare with simpler models.
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