• K Sri Siva Koteswara Rao
• M Lakshmi Lahari
• Ch Jyothi Manikanta Swaroop
• K Sai Harshitha
• Dr.Vijaya Chandra Jadala,Dr.Raja Govindan

This paper presents a comprehensive investigation into the realm of temperature forecasting using Internet of Things (IoT) sensor data. Leveraging a diverse range of techniques from data preprocessing to advanced modeling, our research delves into the intricate dynamics of temperature fluctuations within indoor environments. The study begins with data preprocessing steps, including feature engineering and data cleansing, the integrity and the dataset. , an in-depth analysis of temporal patterns, seasonal variations, and spatial dependencies is conducted to unveil data. The core of our research lies advanced algorithms for temperature forecasting. We employ state-of-the-art methodologies, such as the Prophet forecasting tool, to develop accurate predictive models capable of capturing complex temporal trends and seasonal cycles. Additionally, we explore the integration of domain knowledge, incorporating insights from the IoT domain to enhance the predictive capabilities of our models. Our findings reveal compelling insights into the predictive power of IoT sensor data for temperature forecasting. We demonstrate the effectiveness of our approach through rigorous experimentation and evaluation, showcasing the ability of our models to accurately forecast temperature dynamics over varying time horizons. Moreover, we provide a comparative analysis of different forecasting techniques, highlighting the strengths and limitations of each approach. Overall, this research contributes to the advancement of temperature forecasting methodologies within IoT-driven environments. By leveraging the rich insights derived from IoT sensor data, our study offers valuable implications for diverse applications, including smart buildings, energy management, and climate control systems. We anticipate that our findings will pave the way for future research endeavors aimed at harnessing the full potential of IoT technologies for predictive analytics and decision support in temperature-sensitive domains.

IoT, Temperature forecasting, Sensor data, Data preprocessing, Machine learning, Time series analysis, Seasonal variations, Predictive modeling, Indoor environments, Smart buildings, Energy management, Climate control systems

"Unveiling Patterns and Trends: Time Series Forecasting of Indoor Temperatures with Multiple IoT Data and their Comparison. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d718-d728, April-2024, Available :http://www.jetir.org/papers/JETIR2404393.pdf

"Unveiling Patterns and Trends: Time Series Forecasting of Indoor Temperatures with Multiple IoT Data and their Comparison. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd718-d728, April-2024, Available at : http://www.jetir.org/papers/JETIR2404393.pdf