Forecast 100x faster direct to cloud

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June 12, 2023

Functime

Business forecasting

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Forecast 100x faster direct to cloud

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Overview

Functime is a time-series machine learning tool built to operate at scale. It provides a comprehensive approach to ML forecasting, encouraging users to create end-to-end forecasting pipelines.

It comes equipped with guides and tutorials to assist those new to the field of machine learning. To assess the accuracy and effectiveness of predictions, functime includes scoring, ranking, and plotting functions designed to evaluate numerous forecasts simultaneously.

Notable features include the LLM Forecast Analysts, an AI feature intended to examine and compare patterns, seasonality, and causal factors across any forecast.

It maintains thorough documentation alongside a detailed API reference to ensure a robust understanding of its functionalities. Further, it can be easily installed with pip, and the source code is available on a GitHub repository.

Functime not only offers solutions for forecasting but also empowers its users via education and support, facilitating the understanding and application of machine learning forecasting.

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March 30, 2023

Initial release of Functime.

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Business forecasting Functime

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Pros and Cons

Pros

Forecast 100,000 time series quickly

100x faster than competitors

20x less memory usage

Integrates various external data

Supports exogenous features

In-built causal analysis tools

Feature importance interpretation

Shapley values analysis

Sensitivity analysis tools

GPT-4 enabled trend-seasonality analysis

Easily shareable visualizations

Embeddable visualizations

Fast feature engineering

Supports embarrassingly-parallel forecasts

Robust probabilistic forecasting

Quantile regression techniques

Conformal prediction techniques

Handles zero-inflated datasets

Handles censored forecasts

Blazing-fast hyperparameter tuning

Scores forecasts in parallel

Supports numerous metrics

Easy-to-use API

Efficiently scales in the cloud

View 19 more pros

Cons

Requires API or user-provided data

Limited to business forecasting

Heavy reliance on external data

Possible information overload in visualisations

May struggle with messy datasets

Automatic feature engineering complexity

GPT-4 model intricacy

Requires cloud infrastructure

Database bias towards time-series data

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Q&A

What is Functime?

Functime is an automated machine learning (AutoML) tool designed specifically for business forecasting. It has the capability to forecast, backtest, and score 100,000 time series in under 10 seconds. It incorporates various types of data sources and uses advanced techniques such as quantile regression and conformal prediction for forecasting. Functime also offers GPT-4-enabled trend-seasonality analysis and powerful visualization capabilities using ECharts and Plotly libraries.

What is unique about Functime's speed compared to other tools?

Functime's unique speed is due to its purpose-built design for fast execution and efficient memory usage. It can forecast, backtest, and score 100,000 time series in under 10 seconds, which is 100 times faster than other tools like Amazon Forecast and u8darts. Additionally, Functime uses 20 times less memory as compared to other solutions.

How does Functime integrate external data?

Functime integrates external data by supporting exogenous features and causal analysis. Users can add these features either from API integrations or from their own datasets. This feature allows for a more thorough and relevant analysis by taking into account external factors.

What kind of external data can be integrated with Functime?

Functime allows the integration of various types of external data including holiday, weather, economic, and other relevant external data sources. This provides a more in-depth analysis that goes beyond analyzing numerical trends and includes factors that could have a causal impact.

What is the purpose of Functime's feature importance plots?

Feature importance plots in Functime help in interpreting each forecast. The feature importance plot shows the contribution of each feature to the forecast. This aids in understanding which features are important and how they are contributing to the overall forecast.

How does Functime use Shapley values and sensitivity analysis?

Functime uses Shapley values and sensitivity analysis for a comprehensive interpretation of each forecast. Shapley values are used to distribute the value of the prediction among the contributing features, providing a clear picture of the impact of each feature on the predicted outcome. Sensitivity analysis, on the other hand, gives an idea of how susceptible the output is to changes in input.

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Can you explain more about the GPT-4-enabled trend-seasonality analysis in Functime?

Functime employs GPT-4-enabled trend-seasonality analysis. This is a fully customizable and fine-tuned GPT-4 model used to describe and justify predictions. This feature provides detailed, AI-generated descriptions of why the model made each prediction, adding a layer of interpretability to the model's results.

What kind of visualizations can be created with Functime?

Functime offers sophisticated visualizations using the ECharts and Plotly libraries. These visualizations can be used to represent forecasts, feature importance, Shapley values, sensitivity analysis, and more, providing an easy way to understand the output and performance metrics.

How easy is it to share the visualizations made with Functime?

Visualizations created with Functime can be shared instantly, making it very easy to share. This facilitates easy communication with other data scientists and business stakeholders. The visualizations can also be directly embedded into products.

How does Functime provide fast feature engineering?

Feature engineering in Functime is powered by the world’s fastest DataFrame library, Polars, lazy query optimization, and the Arrow ecosystem. This setup allows for extremely fast execution of feature engineering tasks.

How does Functime handle probabilistic forecasts that scale?

Functime handles probabilistic forecasts that scale by supporting embarrassingly-parallel and robust probabilistic forecasts. It makes use of quantile regression and state-of-the-art conformal prediction techniques to produce robust forecasts that can be scaled efficiently.

What is special about Functime’s zero-inflated and censored forecast options?

Functime's zero-inflated and censored forecast options cater to datasets that have a high number of zeros or missing values. These specialized forecast options ensure the forecast models are robust against such scenarios.

How fast is Functime's hyperparameter tuning?

Functime’s hyperparameter tuning process is described as ‘blazing-fast’, suggesting it can quickly and efficiently optimize the parameters of forecasting models to improve their performance.

Can Functime run and scale time-series machine learning models in the cloud?

Yes, Functime can run and scale time-series machine learning models in the cloud. It is designed to be highly scalable, allowing users to effectively forecast large amounts of time series data.

How easy is it to install and use Functime?

Functime is designed to be easy to install and use. Users can install Functime simply using the command 'pip install functime'. Its interface is built for ease of use, featuring out-of-the-box utilities to add exogenous features and interpret forecasts.

How can users backtest with Functime?

Users can backtest with Functime using its robust forecasting capabilities. The autoML tool allows users to generate forecasts for a specified period, and then compare these forecasts with the actual outcomes in order to evaluate the accuracy and effectiveness of their models.

What does Functime's 100,000 time series capacity mean for users?

Functime's 100,000 time series capacity means that it can process and analyze a large volume of time-series data simultaneously. This makes it suitable for high-scale business forecasting needs where large amounts of related or unrelated time-series need to be forecasted concurrently.

How does Functime handle datasets with messy or numerous zeros?

Functime provides specialized handling for datasets with messy or numerous zeros in the form of zero-inflated and censored forecast options. This ensures that abnormal datasets do not compromise the accuracy of the forecasts.

What metrics does Functime support for scoring forecasts?

Functime supports scoring forecasts with multiple different point and probabilistic metrics. These include MASE and CRPS among others. This allows for a comprehensive view of the quality of the forecasts across multiple dimensions of accuracy.

Can Functime's visualizations be directly embedded into products?

Yes, Functime's visualizations can be directly embedded into products. This feature allows users to seamlessly integrate Functime's data visualizations into their own applications or web platforms.

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