EasyTime: Time Series Forecasting Made Easy
Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like "Which method is best for long term forecasting on time series with strong seasonality?", EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime, we intend to show how it is possible to simplify the use of time series forecasting and to offer better support for the development of new generations of time series forecasting methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesTime Series ForecastingSimilar Papers 제목 키워드 기반
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality
This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method…
Time SeriesTime Series AnalysisTime Series ForecastingEnsembles of Randomized NNs for Pattern-based Time Series Forecasting
In this work, we propose an ensemble forecasting approach based on randomized neural networks. Improved randomized learning streamlines the fitting abilities of individual learners by generating network parameters in acc…
DiversityTime SeriesTime Series AnalysisTime Series ForecastingForecasting Macroeconomic Dynamics using a Calibrated Data-Driven Agent-based Model
In the last few years, economic agent-based models have made the transition from qualitative models calibrated to match stylised facts to quantitative models for time series forecasting, and in some cases, their predicti…
Bayesian InferenceTime SeriesTime Series ForecastingScaling Law for Time Series Forecasting
Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning. Yet, studies on time series forecasting have cast doubt on scaling behaviors of …
Time SeriesTime Series ForecastingExperimental study of time series forecasting methods for groundwater level prediction
Groundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Ma…
ManagementTime SeriesTime Series AnalysisTime Series Forecasting