Brillersys

The Time series Forecasting Application utilizes advanced algorithms to analyze historical data patterns, unlocking hidden potential and insights to accurately forecast future values based on the demand. Businesses and researchers can utilize its capabilities to make informed decisions, mitigate uncertainties, drive strategic planning, and anticipate trends with confidence, particularly in demand forecasting, Resource Planning, Supply Chain Management, Inventory Management, etc,. In a data-driven world, this application becomes an indispensable tool, guiding organizations towards success and a brighter future.

One of the key features of this app is its user-friendly interface, eliminating the need for expertise in data science. Individuals proficient in handling and working with data can effectively utilize this application. This comprehensive guide is tailored to assist you in understanding and leveraging the app's features to forecast your time series data accurately.

The features in the trial version are: Time-series analyzing capabilities Anomaly detection and correction Time-series Forecasting. This trial has limited features and following are the additional features available in the complete version and it can be customized according the need of the customers: Input the data at lowest time granularity available and get the forecasting at different time granularity. Example: Input hourly or transactional data and get hourly/daily/weekly/monthly forecasts.

Able to specify the granularity of the forecast. Select different date ranges for different trainings. Right now, in the free version, it takes whatever data is in the prepared table.

If we need to use different time ranges for forecasting, data needs to be prepared accordingly. Addition of exogenous variables. Time Series Segmentation: This works only with respect to Multiple series.

Time series are segmented/clustered based on the characteristics and modeled based on the segments. Use of wide range of models from statistical forecasting to Machine Learning and Deep Learning Models. The models are customized automatically based on the data.