• Harpinder Kaur

Time series forecasting plays a crucial role in various domains, enabling organizations and individuals to make informed decisions and predictions based on historical data. This abstract provides an overview of different time series forecasting techniques and their applications. Choosing the most appropriate technique depends on factors such as the presence of trend and seasonality, data stationarity, data dimensionality, interpretability requirements, and the need to quantify uncertainty. Assessing the specific characteristics of the data and evaluating the performance of different techniques are crucial in selecting the most suitable approach for time series forecasting. Time series forecasting techniques continue to evolve, and advancements in machine learning and statistical modeling offer new opportunities for accurate and reliable predictions in diverse domains.

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

"Comparative Theoretical Analysis of Time Series Techniques Intended for Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p112-p119, May 2023, Available :http://www.jetir.org/papers/JETIR2305G17.pdf

"Comparative Theoretical Analysis of Time Series Techniques Intended for Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp112-p119, May 2023, Available at : http://www.jetir.org/papers/JETIR2305G17.pdf