Weather Prediction for Tourism Application using Time Series Algorithms
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.