FlowScope: Enhancing Decision Making by Time Series Forecasting based on Prediction Optimization using HybridFlow Forecast Framework
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Abstract (arXiv)
Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare, and finance. Accurately forecasting future trends and patterns is crucial for strategic planning and making well-informed decisions. In this case, it is crucial to include many forecasting methodologies. The strengths of Auto-regressive Integrated Moving Average (ARIMA) for linear time series, Seasonal ARIMA models (SARIMA) for seasonal time series, Exponential Smoothing State Space Models (ETS) for handling errors and trends, and Long Short-Term Memory (LSTM) Neural Network model for complex pattern recognition have been combined to create a comprehensive framework called FlowScope. SARIMA excels in capturing seasonal variations, whereas ARIMA ensures effective handling of linear time series. ETS models excel in capturing trends and correcting errors, whereas LSTM networks excel in reflecting intricate temporal connections. By combining these methods from both machine learning and deep learning, we propose a deep-hybrid learning approach FlowScope which offers a versatile and robust platform for predicting time series data. This empowers enterprises to make informed decisions and optimize long-term strategies for maximum performance. Keywords: Time Series Forecasting, HybridFlow Forecast Framework, Deep-Hybrid Learning, Informed Decisions.
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3 of 12 references.
References · 3
- 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
- 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 273
- 2019A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTMcited by 13
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