Abstract
Macroeconomic forecasting is a key task of developing the outlook for economy of a country and supporting decision making. In this paper we present a novel approach to macroeconomic forecasting based on LSTM-based encoder-decoder and conditional normalizing flows. First, we employ LSTM-based encoder-decoder to learn vector representations of the input data. The obtained representations are then transformed by using conditional normalizing flows. In such a way, the distribution of the data encoded in the representations is transformed into a more complex distribution. We evaluate the proposed approach on 215 macroeconomic variables of FRED-QD dataset. In experiments, we use two kinds of normalizing flows: Real-valued Non-Volume Preserving and Masked Autoregressive Flow. Experimental results show that proposed approach outperforms VAR in several orders of magnitude and also outperforms DeepVAR on the benchmark dataset. To the best of our knowledge, this is the first time LSTM-based encoder-decoder in combination with conditional normalizing flows has been successfully adopted to forecast hundreds of macroeconomic variables simultaneously.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Image taken from https://colah.github.io/posts/2015-08-Understanding-LSTMs/.
- 2.
- 3.
- 4.
We employ VAR and LASSO using sciKit-learn at https://scikit-learn.org.
- 5.
We use DeepVAR, LSTM-RealNVP, and LSTM-MAF implemented at https://github.com/zalandoresearch/pytorch-ts in experiments.
References
Alexandrov, A., et al.: GluonTS: probabilistic and neural time series modeling in python. J. Mach. Learn. Res. 21(116), 1–6 (2020)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Benidis, K., et al.: Neural forecasting: Introduction and literature overview. arXiv preprint arXiv:2004.10240 (2020)
Bolhuis, M., Rayner, B.: Deus ex machina? A framework for macro forecasting with machine learning. In: IMF Working Papers, vol. 2020(45). International Monetary Fund (2020). https://doi.org/10.5089/9781513531724.001
Chakraborty, C., Joseph, A., et al.: Machine learning at central banks. Technical report, Bank of England (2017)
Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014)
Cook, T.R., Smalter Hall, A.: Macroeconomic indicator forecasting with deep neural networks. In: Proceedings of 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018), pp. 261–261 (2018)
Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. arXiv preprint arXiv:1605.08803 (2016)
Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Deep learning for time series classification: a review. Data Mining Knowl. Discov. 33(4), 917–963 (2019)
Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102(477), 359–378 (2007)
Hall, A.S.: Machine learning approaches to macroeconomic forecasting. Econ. Rev. 103(4), 63 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of International Conference on Machine Learning, pp. 448–456 (2015)
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)
Jung, J.K., Patnam, M., Ter-Martirosyan, A.: An algorithmic crystal ball: forecasts-based on machine learning. International Monetary Fund (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lim, B., Zohren, S.: Time series forecasting with deep learning: a survey. arXiv preprint arXiv:2004.13408 (2020)
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: 100,000 time series and 61 forecasting methods. Int. J. Forecast. 36(1), 54–74 (2020)
McCracken, M., Ng, S.: FRED-QD: a quarterly database for macroeconomic research. Technical report, National Bureau of Economic Research (2020)
McCracken, M.W., Ng, S.: FRED-MD: a monthly database for macroeconomic research. J. Bus. Econ. Stat. 34(4), 574–589 (2016)
Medeiros, M.C., Vasconcelos, G.F., Veiga, A., Zilberman, E.: Forecasting inflation in a data-rich environment: the benefits of machine learning methods. J. Bus. Econ. Stat. 39, 98–119 (2019)
Papamakarios, G., Pavlakou, T., Murray, I.: Masked autoregressive flow for density estimation. In: Proceedings of Advances in Neural Information Processing Systems, pp. 2338–2347 (2017)
Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 51(5), 1–36 (2018)
Rasul, K., Sheikh, A.S., Schuster, I., Bergmann, U., Vollgraf, R.: Multi-variate probabilistic time series forecasting via conditioned normalizing flows. arXiv preprint arXiv:2002.06103 (2020)
Sagheer, A., Kotb, M.: Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems. Sci. Rep. 9(1), 1–16 (2019)
Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181–1191 (2020)
Shih, S.Y., Sun, F.K., Lee, H.y.: Temporal pattern attention for multivariate time series forecasting. Mach. Learn. 108(8-9), 1421–1441 (2019)
Smyl, S.: A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. Int. J. Forecast. 36(1), 75–85 (2020)
Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutorials 21(3), 2224–2287 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Nguyen, H.T. et al. (2022). Macroeconomic Forecasting Based on LSTM-Conditioned Normalizing Flows. In: Ngoc Thach, N., Ha, D.T., Trung, N.D., Kreinovich, V. (eds) Prediction and Causality in Econometrics and Related Topics. ECONVN 2021. Studies in Computational Intelligence, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-77094-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-77094-5_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77093-8
Online ISBN: 978-3-030-77094-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)