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A hybrid Facebook Prophet-ARIMA framework for forecasting high-frequency temperature data
High-frequency temperature data, such as hourly or daily measurements, show complex seasonal patterns and short-term dynamics that affect various...
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An innovative hybrid W-EEMD-ARIMA model for drought forecasting using the standardized precipitation index
Drought, a critical consequence of water scarcity and climate change, profoundly impacts human life. This study introduces a new W-EEMD-ARIMA hybrid...
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A novel global average temperature prediction model——based on GM-ARIMA combination model
In recent years, under the influence of changes in natural conditions and human social activities, the issue of global warming has become...
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Combined BiLSTM and ARIMA models in middle- and long-term polar motion prediction
As one of the main components of the Earth orientation parameters, short-term prediction of the geodetic polar motion series is crucial in the field...
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Earthquake magnitude prediction in Turkey: a comparative study of deep learning methods, ARIMA and singular spectrum analysis
The Aegean region is geologically situated at the western end of the Gediz Graben system, influenced by the Western Anatolian Regime. In addition,...
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Assessing the changing trends of groundwater level with spatiotemporal scale at the northern part of Bangladesh integrating the MAKESENS and ARIMA models
The study aims to assess the pattern of regional and temporal changes in groundwater levels from 2001 to 2020 and to forecast regional groundwater...
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Forecasting reservoir inflow combining Exponential smoothing, ARIMA, and LSTM models
Reservoir inflow prediction is a key factor to flood control decisions and is also crucial concerning the operational planning and scheduling of the...
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ARIMA model simulation for total electron content, earthquake and radon relationship identification
Earthquakes cause many losses of life and property with their devastating effects. Scientists conduct studies to predict the hazards by examining the...
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Stochastic (S[ARIMA]), shallow (NARnet, NAR-GMDH, OS-ELM), and deep learning (LSTM, Stacked-LSTM, CNN-GRU) models, application to river flow forecasting
Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this...
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Modeling of rainfall time series using NAR and ARIMA model over western Himalaya, India
The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian...
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Urban sprawl modelling and prediction using regression and Seasonal ARIMA: a case study for Vellore, India
Analysing the changes in various landuses during the past, identifying the direction and type of urban sprawl, modelling its causative factors and...
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Time series analysis of climate variables using seasonal ARIMA approach
The dynamic structure of climate is governed by changes in precipitation and temperature and can be studied by time series analysis of these factors....
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Assessing and forecasting of groundwater level fluctuation in Joypurhat district, northwest Bangladesh, using wavelet analysis and ARIMA modeling
Groundwater resource plays a crucial role for agricultural crop production and socio-economic development in some parts of the world including...
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Examining the Effect of COVID-19 on rail freight volume and revenue using the ARIMA forecasting model and assessing the resilience of Indian railways during the pandemic
India implemented a nationwide lockdown on 22 March 2020 to prevent Coronavirus (COVID-19) from spreading throughout the nation. Only the most...
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Prediction of Seasonal Rainfall with One-year Lead Time Using Climate Indices: A Wavelet Neural Network Scheme
This paper presents the development of the Wavelet Artificial Neural Networks (WANN) model to forecast seasonal rainfall in Queensland, Australia,...
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Examining and predicting the influence of climatic and terrestrial factors on the seasonal distribution of ozone column depth over Tehran province using satellite observations
When combined with conducive atmospheric conditions, air pollution caused by fossil fuel consumption associated with transportation, industry and...
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Comparisons of autoregressive integrated moving average (ARIMA) and long short term memory (LSTM) network models for ionospheric anomalies detection: a study on Haiti (Mw = 7.0) earthquake
Since ionospheric variability changes dramatically before the major earthquakes (EQ), the detection of ionospheric anomalies for EQ forecasting has...
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Estimation of ARIMA model parameters for drought prediction using the genetic algorithm
Drought is a phenomenon that occurs slowly, affecting surface water and groundwater resources, which can reduce the water supply, worsen water...
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Times Series Forecasting of Monthly Rainfall using Seasonal Auto Regressive Integrated Moving Average with EXogenous Variables (SARIMAX) Model
In this study, the monthly rainfall time series forecasting was investigated based on the effectiveness of the Seasonal Auto Regressive Integrated...
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Ionospheric anomalies detection using autoregressive integrated moving average (ARIMA) model as an earthquake precursor
The ARIMA method, time series analysis technique, was proposed to perform short-term ionospheric Total Electron Content (TEC) forecast and to detect...
