Time Series Forecasting: Statistical Model to Predict Future Value

Abstract

In data analysis and predictive modelling, time series forecasting is a vital area of research. The application of statistical models to time series data prediction is explored. It is difficult to effectively analyses and forecast time series data because of its temporal reliance. To meet this challenge, a number of statistical models have been created, ranging from straightforward procedures like exponential smoothing to more sophisticated ones like autoregressive integrated moving average (ARIMA) and machine learning-based methods like recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. This emphasizes the significance of time series forecasting, summarizes the basic statistical models applied in this field, evaluates their advantages and disadvantages, and stresses the need of precise predictions for decision-making across a variety of fields, including finance, economics, meteorology, and more. The key statistical models used in this domain are also described, along with their strengths and weaknesses. The summary ends by highlighting the continuous research and improvements in time series forecasting techniques, highlighting their critical role in extrapolating future patterns and making wise judgements in a society that is becoming more and more data-driven.