futureexpert 0.12.0

Forecasting has never been easier.

futureEXPERT

futureEXPERT is a flexible Python toolkit designed to significantly simplify the process of building professional forecasting solutions. It is built upon a Smart Build principle: a clear division of tasks that makes powerful forecasting accessible with ease, even without a deep data science background.

• You focus on the "what": Designing the solution tailored to your specific domain and requirements, connecting your data and integrating the results into your workflow.
• futureEXPERT handles the "how": The complex methodological and technical details, from data preparation to forecast generation, are abstracted away for you.

The workflow is handled by four distinct modules:

1. CHECK-IN: Prepares your time series data. This module validates, cleans, and transforms your input data to ensure it's ready for forecasting.
2. POOL: Provides a library of curated external variables (e.g., economic indicators, weather data). You can search this continuously updated collection to find useful covariates for your forecast.
3. MATCHER: Ranks covariates to find the most impactful ones for your data. It takes your own covariates or variables from the POOL, determines their optimal time lag, and measures their predictive value against a baseline model.
4. FORECAST: Generates the final forecast. This module automatically selects the best model (from statistical, ML, and AI methods) for each time series and can incorporate the top-performing covariates identified by MATCHER.

The simplest workflow only contains CHECK-IN and FORECAST is described in the jupyter notebook getting started.

In case you don't want to use this Python client or access futureEXPERT via API, check out our frontend solution futureNOW.

Registration

If you do not have an account for future yet, click here to register for a free account.

Installation

In order to use futureEXPERT, you need a Python environment with Python 3.9 or higher.

The futureEXPERT package can be directly installed with pip from our GitHub repository.

pip install -U futureexpert

Getting started

To get started with futureEXPERT we recommend checking out the jupyter notebook getting started to help you with your first steps. Also check our quick start video tutorial.

Ready-made use case templates

Utilize our use case templates to get started with your own business application right away.

• Demand Planning
• Sales Forecasting

Advanced usage

• checkin configuration options - Different options to prepare your data to time series.

• Advanced workflow FORECAST - For more control about the single steps for generating a forecast.

• Using covariates for FORECAST - Create forecasts with covariates by using your own data of influencing factors.

• Using covariates - MATCHER and FORECAST - Using covariates: Leverage MATCHER to identify predictive covariates, get ranking of all covariates with the best time lag & incorporate the result into your FORECAST.

• Using covariates from POOL - How to use potential influencing factors from POOL.

• Working with results - Overview of forecast result functions (e.g. export, plotting) and how to use them; further detailed information about the results (e.g. summary of forecasting methods).

• API documentation - Further information about all features and configurations.

Video tutorials

Check out our video tutorials for a quick introduction to various aspects of futureEXPERT.

• Getting started from registration to first forecasts within minutes.
• CHECK-IN your data and create time series for your forecasting use case.

Contributing

You can report issues or send pull requests in our GitHub project.

Wiki for prognostica employees

Further information for prognostica employees can be found here