Summary of Flowscope: Enhancing Decision Making by Time Series Forecasting Based on Prediction Optimization Using Hybridflow Forecast Framework, By Nitin Sagar Boyeena et al.
FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework
by Nitin Sagar Boyeena, Begari Susheel Kumar
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive framework called FlowScope has been proposed, combining the strengths of various forecasting methodologies. The framework integrates Auto-regressive Integrated Moving Average (ARIMA), Seasonal ARIMA models (SARIMA), Exponential Smoothing State Space Models (ETS), and Long Short-Term Memory (LSTM) Neural Network model to create a robust platform for predicting time series data. SARIMA excels in capturing seasonal variations, while ARIMA handles linear time series effectively. ETS models excel in capturing trends and correcting errors, whereas LSTM networks reflect intricate temporal connections. By combining these methods from both machine learning and deep learning, FlowScope offers a versatile and robust approach for time series forecasting, empowering enterprises to make informed decisions and optimize long-term strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlowScope is a new way to predict what will happen in the future. It uses different techniques together to get better results. This includes Auto-regressive Integrated Moving Average (ARIMA), Seasonal ARIMA models (SARIMA), Exponential Smoothing State Space Models (ETS), and Long Short-Term Memory (LSTM) Neural Network model. Each of these methods is good at something, like capturing seasonal patterns or handling errors. By combining them all, FlowScope can predict time series data more accurately than any one method alone. This helps businesses make better decisions and plan for the future. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Machine learning » Neural network » Time series
