An Improved Adaptive Prediction Model for Evolving Time Series Data Streams

Smart home settings have gotten a lot of attention lately because they can save energy, make people more comfortable, and give them access to advanced control features. But it's hard for these systems to make accurate predictions about time series data, especially when the data is subject to concept drift, which means that the patterns that lie beneath the data change over time. This study paper shows a useful Recurrent Neural Network (RNN)-based model for predicting time series. It includes idea drift adaptation methods that are especially made for smart home uses. The main goal of this study is to find solutions to the problems that standard predicting methods have when they try to work in smart homes, which are always changing. We find a very important study gap: we need strong models that can quickly and accurately pick up on changes in user behavior, external factors, and system dynamics, all without requiring a lot of retraining. To close this gap, we suggest a more advanced RNN design using long short-term memory (LSTM) units to find time correlations in the data and a new way to identify drift. Our method uses cutting-edge time series predicting methods along with real-time tracking of data properties. We use a flexible system to find changes in the way the data is distributed. This lets the model change its settings and make better predictions. We test our proposed model's usefulness by doing detailed experiments with real-life datasets from smart home systems. These studies focus on things like energy use, changes in temperature, and how users interact with the system. Our RNN-based model does a much better job of predicting the future than standard methods, as shown by the results. It is much more accurate and reliable. The idea drift adaptation also makes the model more resistant to changes, which makes sure that it performs the same way over time. The knowledge base on smart home technology is growing, and this study adds to it by finding a flexible and effective way to predict time series in real time.