Weather Prediction for Tourism Application using Time Series Algorithms
Abhijit Kocharekar1, Bharat Nemade2, Chetan Patil3, Durgesh Sapkale4, Sagar Salunke5

1Abhijit Kocharekar*, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
2Bharat Nemade, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
3Chetan Patil, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
4Durgesh Sapkale, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
5Prof. Sagar Salunke, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.
Manuscript received on August 22, 2020. | Revised Manuscript received on September 05, 2020. | Manuscript published on September 10, 2020. | PP: 406-412 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.I7003079920 | DOI: 10.35940/ijitee.I7003.0991120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Precise projections of future events are crucial in many areas, one of which is the tourism sector. Usually counter-trials and towns spend a enormous quantity of cash in planning and preparation to accommodate (and benefit) visitors. Precisely predicting the amount of visits in the days or months, that follow would benefit the economy and tourists both. Previous studies in this field investigate predictions for a nation as a whole rather than for fine-grained fields within a nation. Weather forecasting has drawn the attention of many scientists from distinct research communities due to its impact on human life globally. The developing deep learning methods coupled with the wide accessibility of huge weather observation data and the advancement of machine learning algorithms has motivated many scientists to investigate hidden hierarchical patterns for weather forecasting in large amounts of weather data over the previous century. To predict climate information accurately, heavy statistical algorithms are used on the big quantity of historical information. Time series Analysis enables us know the fundamental forces leading to a specific trend in time series data points and enables us to predict and monitor information points by fitting suitable models into them. In this study, Holt-Winter model is used for predicting time series. The forecasting algorithm for Holt-Winters enables users to construct a time series and then use that data to forecast interest areas. Exponential smoothing allocates weights and their respective values against past data to decrease exponentially, to decrease the weight value for older data. 
Keywords: Tourism Industry, Weather Forecasting, Time Series Analysis, Holt-Winter algorithm.