A Comparative Study and Analysis of Time Series Forecasting Techniques

Abstract

Time series data abound in many realistic domains. The proper study and analysis of time series data help to make important decisions. Study of such data is very useful in many applications where there are trendy changes with time or specific seasonality as in electricity demand, cloud workload, weather and sales, cost of business products, etc. By understanding the nature of the time series and the objective of analysis, we have used different approaches to learn and extract meaningful information that can satisfy the business needs. The present paper covers and compares various forecasting algorithmic approaches and explores their limitations and usefulness for different types of time series data in different domains. Keywords Time series forecast • Deep learning • ARIMA • MVFTS • CNN • LSTM • CBLSTMThis article is part of the topical collection "Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications" guest edited by Bhanu Prakash K N and M. Shivakumar.

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Online Tuning of Hyperparameters in Deep LSTM for Time Series Applications

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