• Harpinder Kaur

Time series forecasting is a crucial task in various domains, ranging from finance and economics to weather prediction and demand forecasting. Over the years, numerous techniques have been developed to address the challenges associated with modeling and predicting time-dependent data. This paper presents a comprehensive review and comparative analysis of different techniques for time series forecasting. The research paper introduces traditional statistical methods, including Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Exponential Smoothing. These methods have long been the foundation for time series analysis and provide a solid baseline for forecasting. Their strengths lie in their simplicity, interpretability, and ability to capture different components of time series data. The research paper elaborates on the Neural Networks which excel at capturing complex patterns, non-linear relationships, and interactions among variables. Neural Networks offer the potential for improved accuracy and flexibility in handling large-scale and high-dimensional time series datasets. Bayesian methods approaches incorporate prior knowledge, handle uncertainties, and provide a coherent framework for probabilistic forecasting. They are particularly useful in situations where prior information or expert opinions are available. The advantages and limitations of each technique are discussed, considering factors such as computational complexity, data requirements, interpretability, and the presence of external factors or seasonality. Furthermore, real-world applications across various domains are highlighted to illustrate the practical relevance and effectiveness of these techniques in different contexts. In conclusion, the research paper provides a comprehensive overview of different techniques for time series forecasting, empowering researchers and practitioners to make informed decisions regarding the selection and application of appropriate methods.

ARIMA, Bayesian methods, Neural Networks, Prophet, Spectral Analysis, VAR

"A Comprehensive Review on Time Series Forecasting Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p103-p111, May 2023, Available :http://www.jetir.org/papers/JETIR2305G16.pdf

"A Comprehensive Review on Time Series Forecasting Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp103-p111, May 2023, Available at : http://www.jetir.org/papers/JETIR2305G16.pdf