nextpred

nextpred is a software library designed for time series forecasting. It aims to simplify the process of building and deploying predictive models for sequential data. The library provides a unified interface to various forecasting algorithms, allowing users to experiment with different methods without extensive code modifications.

The core functionality of nextpred revolves around automating common time series forecasting tasks. This includes data preprocessing, feature engineering, model selection, training, and evaluation. Users can typically input their time series data and specify desired forecasting horizons or model configurations. The library then handles the underlying complexity of fitting models to the data and generating future predictions.

nextpred supports a range of forecasting techniques, often encompassing both traditional statistical methods and more modern machine learning approaches. Examples of algorithms that might be integrated include ARIMA, Exponential Smoothing, Prophet, and potentially neural network-based models like LSTMs. The library's design often emphasizes ease of use and rapid prototyping, making it suitable for users who need to quickly implement forecasting solutions.

The target audience for nextpred includes data scientists, analysts, and developers who work with time series data across various domains. This could range from financial forecasting and demand prediction to sensor data analysis and weather forecasting. By abstracting away much of the intricate implementation details, nextpred seeks to make advanced forecasting capabilities more accessible.