International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | Jun 2022
p-ISSN: 2395-0072
www.irjet.net
Time Series Weather Forecasting Techniques: Literature Survey Janhavi Patil1, Prof. Nirmala Shinde2 1MTech
Computer Engineering Student, K J Somaiya College of Engineering, Mumbai, Maharashtra, India Dept. of Computer Engineering, K J Somaiya College of Engineering, Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Professor,
Abstract - Forecasting is a part of statistical modelling that
2 Methodology
is widely used in several fields because of its benefits in decision-making. The purpose of forecasting is to predict the future values of certain variables that range with time using its previous values. Time Forecasting is related to the formation of models and methods that can be used to produce a good forecast. This research is survey paper research that used a systematic mapping study and systematic literature review. Generally, time series forecasting uses linear time series models, specifically the ARIMA model and LSTMthat has long been used because it has good forecasting accuracy. The goal of this research is to review time series forecasting methods such as ARIMA, Prophet and LSTM and analyze the working of time series forecasting methods. It also discusses the approaches of different methods used in time series forecasting. Its goal is to increase the amount of awareness regarding time series forecasting and its methods.
The method used structured mapping study and structured literature review conducted by recognizing and interpreting the clarifying in the literature review in accordance with the topic time series forecasting raised in this paper. The univariate time series made up of a single result over a time period. The multivariate time series made up of more than one result collected over time. Multivariate time series analysis examination is more challenging compared to univariate time series analysis.
Key Words: Time Series models, ARIMA, LSTM, Prophet, Accuracy, Forecasting
1.INTRODUCTION Fig -1: Time Series forecasting Methods and Models
Time series data forecasting is a part of statistical modelling that is widely used in various departments such as weather stations, Finance, banking, healthcare departments such as covid-19 data analysis because of its benefits in decisionmaking. Time series forecasting analysis has several objectives, namely, forecasting, modelling, and manage. Forecasting is an element that is important in managing activities because whether or not an effective decision is made depends on several factors that influence, although hidden, when a decision is taken.
Literature review related to the use and development of time series forecasting models from various studies in various departments then calculated to find timelessness and the latest developments from each method used.
3 Forecasting Model The research methodology was studied to assess the accuracy of different types of time series models for rainfall, covid data, real- estate, Bit-coin forecasting. Initially, a comprehensive literature survey was carried out to study related research conducted to identify the techniques, datasets and observations of the different methodologies implemented worldwide. Often used time series forecasting models were identified from the literature survey and the models were developed to forecast the rainfall Bit-coin forecasting, covid-19 data, real-estate.
The purpose of time series forecasting model is to predict the upcoming values of certain variables that range with time using its previous values. Forecasting is related to the generation of models and methods that can be used to construct a good forecast. In time series data, the doings of past events can be used for forecasting because it is expected that, in the future, the impact of the doings of past events will still occur. The advantages of forecasting can be felt in many fields, including production, marketing, economics and finance. Generally, time series research uses linear time series models, specifically the autoregressive integrated moving average (ARIMA) model, Prophet and LSTM.
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3.1 Forecasting with ARIMA The first phase in applying ARIMA model is to check whether the time series is stationary or not. Autoregressive Integrated Moving Average works best when data has a fixed design
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