[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
Atharva Naik, Ninad Mungekar, Surabhi Pandit, Parth Vyavahare, Suresh Mestry,
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student,, Student,, Student,, Student,, Assistant Professor, Rajiv Gandhi Institute of Technology (affiliated to the University of Mumbai), Mumbai,, Rajiv Gandhi Institute of Technology (affiliated to the University of Mumbai), Mumbai,, Rajiv Gandhi Institute of Technology (affiliated to the University of Mumbai), Mumbai,, Rajiv Gandhi Institute of Technology (affiliated to the University of Mumbai), Mumbai,, Rajiv Gandhi Institute of Technology (affiliated to the University of Mumbai), Mumbai, Maharashtra,, Maharashtra,, Maharashtra,, Maharashtra,, Maharashtra, India, India, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nThe accurate prediction of time series data holds substantial significance in various fields, enabling informed decision-making and resource optimization. In this study, temperature variations over time are predicted using the Autoregressive Integrated Moving Average (ARIMA) model. Reliable temperature projections are more important now than ever because of climate change and its effects. For time series prediction problems, the ARIMA model—which is well-known for its ability to capture temporal dependencies in data—can be applied. It is possible to apply and adjust this model to take into account the unique features of temperature data, like trends and seasonality. To ensure quality and consistency, historical temperature data is gathered and pre-processed. Using machine learning algorithms, the suggested works forecast the weather based on variables like temperature, wind, and humidity. The weather prediction industry has had success with computer-aided prediction systems that use machine learning models. The experiment emphasises the value of applying cutting-edge data analysis methods to practical problems and shows how larger prediction systems may be refined even further.
n
Keywords: Weather Forecasting, ARIMA, Time Series data.
n[if 424 equals=”Regular Issue”][This article belongs to Research & Reviews : Journal of Space Science & Technology(rrjosst)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n
nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- Arati Gangshetty, Gurpreet Kaur, Uttam Malunje (Nov 2021): Time Series Prediction of Temperature in Pune using Seasonal ARIMA Model (Vol.10 Issue 11) IJERT
- Neeraj Kumar, Govind Kumar (2013): A Time Series ANN Approach for Weather Forecasting.
- Mehmet Tektas, Weather Forecasting using ANFIS and ARIMA models:case study for Instanbul
- Box G.E.P. and G.M. Jenkins, 1976: Time series analysis: Forecasting and Control, Holden day Inc.,CA
- Małgorzata Murat, Iwona Malinowska, Magdalena Gos, and Jaromir Krzyszczak (2018): Forecasting daily meteorological time series using ARIMA and regression models
- Peng Chen et al 2018 IOP Conference Series: Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing
- Prabhanshu Attri, Yashika Sharma, Kristi Takach, Falak Shah, 2022: Timeseries forecasting for weather prediction.
- Nikita Shivhare, Atul Kumar Rahul, Shyam Bihari Dwivedi and Prabhat Kumar Singh Dikshit (Nov 2018): ARIMA based daily weather forecasting tool: A case study for Varanasi.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
Research & Reviews : Journal of Space Science & Technology
n
n
n
n
nnn
n
| Volume | 13 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 6, 2024 | |
| Accepted | July 24, 2024 | |
| Published | August 16, 2024 |
n
n
n
n
n
n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]