Identifying Time-Spotting

Abstract

In numerous fields, including finance, economics, and healthcare, time- series forecasting is an indispensable instrument for predicting future trends and making informed decisions. This paper introduces time-sight, an application that utilizes the prophet library to provide a comprehensive framework for time-series forecasting. Time-sight provides users with the capability to load and preprocess time-series data, set up the forecasting model with a range of parameters including horizon, seasonality, trend components, and holidays, generate forecasts after fitting the model, assess and validate the predictions through cross-validation, adjust hyperparameters, and export the forecast, model metrics, and configuration for additional analysis. A number of the pre-processing and modeling duties are automated by our application, which renders it accessible to users of differing degrees of proficiency. Using real-world datasets, we illustrate the capabilities of time-sight and discuss its potential applications and limitations. We hold the expectation that our application will facilitate more precise and well-informed decision-making by serving as a potent time-series forecasting instrument across diverse domains.