Time Series Analyzation and Prediction of Climate using Enhanced Multivariate Prophet

Time Series Analyzation and Prediction of Climate using Enhanced Multivariate Prophet

© 2021 by IJETT Journal
Volume-69 Issue-10
Year of Publication : 2021
Authors : J. Jagannathan, Dr. C. Divya
DOI :  10.14445/22315381/IJETT-V69I10P212

How to Cite?

J. Jagannathan, Dr. C. Divya, "Time Series Analyzation and Prediction of Climate using Enhanced Multivariate Prophet," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 89-96, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P212

In a development of a country, a huge challenge faced was Climatic change. For an agriculturebased country like India, it affects the quality and quantity of the products. Increase in the temperature and decrease in the rainfall. The main factor behind this was the urbanization of many cities. Based on the increase in the industries and density of population, the heat island was created, and it affects the climate dynamically. But in the last two years, the climate seems to be improving, and it was due to the lockdown effect. The Prediction of temperature with more accuracy is a great challenge. Here the temperature is a factor that can be affected by various other factors, mainly humidity, wind speed, precipitation. In this paper, a multivariate prediction of temperature was proposed. Here the historical climatic data has been taken with a dataset of temperature, wind speed, humidity, precipitation, and pressure. The data has been trained and tested with various time series algorithms like Auto- ARIMA, LSTM, Prophet models. And a proposed Enhanced Multivariate Prophet (EMP) algorithm has been employed to find the seasonality and trend. Based on the analyzation the temperature has been forecasted for the future 365 days. In comparison with the other algorithms. The testing of EMP provides a very good accuracy of 99.9%. MAE and RMSE of 0.02.

Auto-ARIMA, LSTM, Prophet, Temperature, Wind Speed, Precipitation, Humidity.

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