Integrated Statistical, Machine Learning, and Deep Learning Model for Time Series Forecasting

Abstract

The proposed invention integrates statistical models, machine learning algorithms, and deep learning techniques for advanced time series forecasting. The system combines traditional statistical methods like ARIMA for linear trend analysis, machine learning algorithms for capturing non-linear relationships, and deep learning models, such as LSTM and GRU, for modeling long-term dependencies and sequential data. This hybrid approach enhances forecasting accuracy and adaptability, making it suitable for dynamic, noisy, and complex datasets encountered in industries like finance, healthcare, retail, energy, and manufacturing. By leveraging the strengths of each technique, the invention provides more accurate, flexible, and scalable solutions for real-time predictions across various forecasting horizons. The system addresses the limitations of existing methods, ensuring robust predictions even in rapidly changing environments, while offering transparency and interpretability. The invention is highly adaptable, hand

Specification

Description:The present invention pertains to the field of data science and predictive analytics, specifically focusing on the integration of statistical methods, machine learning, and deep learning models for time series forecasting. It combines traditional statistical approaches, such as autoregressive integrated moving average (ARIMA), with modern machine learning algorithms like decision trees, support vector machines, and ensemble models. Additionally, it incorporates deep learning techniques, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), to improve predictive accuracy and handle complex temporal patterns. This integrated model aims to enhance the forecasting process by utilizing the strengths of each methodology in a complementary manner, providing a more robust, efficient, and scalable solution. It can be applied to various industries, including finance, healthcare, retail, energy, and manufacturing, where accurate time series predicti , Claims:1. A system for time series forecasting comprising statistical models, machine learning algorithms, and deep learning models, wherein the statistical model captures linear trends, the machine learning algorithms handle non-linear relationships, and the deep learning models capture long-term temporal dependencies, thereby providing enhanced forecasting accuracy.
2. The system of claim 1, wherein the statistical model comprises an ARIMA model for modeling linear trends and seasonal variations within the time series data.
3. The system of claim 1, wherein the machine learning algorithms include decision trees, support vector machines, or ensemble methods for handling non-linear relationships and complex patterns in the data.
4. The system of claim 1, wherein the deep learning models include long short-term memory (LSTM) networks or gated recurrent units (GRU) for capturing long-term dependencies and sequential patterns in time series data.
5. The system of claim 1, wherein the machine learning models are used in

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