Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predicting Climatic Variables on Local Level with SARIMA, LSTM and Hybrid Techniques

Version 1 : Received: 9 May 2022 / Approved: 11 May 2022 / Online: 11 May 2022 (05:50:41 CEST)

How to cite: Parasyris, A.; Alexandrakis, G.; Kozyrakis, G.V.; Spanoudaki, K.; Kampanis, N.A. Predicting Climatic Variables on Local Level with SARIMA, LSTM and Hybrid Techniques . Preprints 2022, 2022050147 (doi: 10.20944/preprints202205.0147.v1). Parasyris, A.; Alexandrakis, G.; Kozyrakis, G.V.; Spanoudaki, K.; Kampanis, N.A. Predicting Climatic Variables on Local Level with SARIMA, LSTM and Hybrid Techniques . Preprints 2022, 2022050147 (doi: 10.20944/preprints202205.0147.v1).

Abstract

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

Keywords

SARIMA; Artificial Neural Networks (ANN); LSTM; hybrid methodologies; prediction

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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