Time Series Modeling with Deep Neural Networks

Çağatay Bal and Çağdaş Hakan Aladağ

Chapter 9 in Modeling and Advanced Techniques in Modern Economics, 2022, pp 187-209 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Deep neural networks are the latest among powerful artificial intelligence tools. As advanced forms of artificial neural networks, deep nets can be used in various fields and also time series forecasting. Time series forecasting is a major domain which extends to almost all problem-wise applications. Because of this reason, powerful tools as deep networks have become the perfect tools with their modular structure for time series forecasting. In this study, starting from shallow neural networks to advanced deep networks, including convolutional nets and long short-term memories, in-depth analytics are investigated and their results are given with applications and Python codes.

Keywords: Harmonic Regression; Periodograms; Consumer Price Index; Food Inflation; Turkey; Gaussian Distribution; Europe Union; GDP; Panel Data; Spatial Regression; Measurement Errors; Nonlinear Time Series; Chaotic Time Series; Weibull Distribution; Location Parameters; Fiducial Approach; Hypothesis Testing; Green Swan; Financial Stability; Annex II Countries; Financial Time Series; Kernels; Stock Index; Machine Learning; Statistical Learning; Optimization; WSAR Algorithm; Deep Neural Networks; Phyton; Parameter Estimation; COVID-19; Clustering Analyses; Artificial Neural Networks; Performance Criteria; Time Series Forecasting; Statistical Inference (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 C6 C63 (search for similar items in EconPapers)
Date: 2022
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