Time series data is prevalent in various sectors, including weather forecasting, business strategizing, and complex systems monitoring. Effective processing of this data can aid in areas like strategic business planning and anomaly detection. Despite the availability of numerous tools for time series analysis, their complexities often pose challenges to the user. Addressing this issue, a company called Unit8 has developed and open-sourced a new Python tool called Darts, designed to simplify time series processing and forecasting.
The landscape of time-series data tools can often be fragmented, requiring different libraries for each subsequent process. Data preprocessing might require Pandas, Facebook Prophet for forecasting, statsmodels for detecting seasonality, and custom scripts for backtesting and model selection. Such a workflow can complicate the process of incorporating more advanced models. Hence, there exists a need for a user-friendly solution that provides a streamlined and consistent experience.
Darts serve as a Python library analogous to scikit-learn for time series analysis. It offers a consistent API, encapsulating various functionality, including data manipulation, model fitting, forecasting, and backtesting, into a single framework. As a result, users can effortlessly switch between models and approaches without dealing with compatibility issues.
Darts’ core consists of the TimeSeries data type, designed to represent multivariate and probabilistic time series, ensuring they are accurately formed with a proper time index. The library interface echoes that of scikit-learn with a fit() method for model training and a predict() method used for making predictions. This consistent interface supports experimentation with models ranging from traditional ones like Exponential Smoothing and Auto-ARIMA to advanced neural network-based models like RNNs and Transformers. Darts supports both univariate and multivariate time series, producing deterministic or probabilistic forecasts.
Darts offers a wide array of built-in models, provides tools for backtesting and model evaluation, and supports advanced features like probabilistic filtering and hyperparameter tuning. The library’s design promotes a functional programming style. Darts simplifies model training, forecasting, and evaluation within time series analysis by offering a streamlined framework. With its unified and consistent API, Darts enhances user experience, making it useful for data scientists and analysts working with time series data. As an open-source development, Darts will continue to evolve based on community contributions.
