Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting
- Title
- Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting
- Creator
- Khanna V.; Joshua J.; Pramila R.M.
- Description
- There has been a recent deep learning revolution in Computer Vision and Natural Language Processing. One of the biggest reasons for this has been the availability of large-scale datasets to pre-train on. One can argue that the Time Series domain has been left out of the aforementioned revolution. The lack of large scale pretrained models could be one of the reasons for this.While there have been prior experiments using pre-trained models for time series forecasting, the scale of the dataset has been relatively small. One of the few time series problems with large scale data available for pre-training is the financial domain. Therefore, this paper takes advantage of this and pretrains a ID CNN using a dataset of 728 US Stock Daily Closing Price Data in total, 2,533,901 rows. Then, we fine-tune and evaluate a dataset of the NIFTY 200 stocks' Closing Prices, in total 166,379 rows. Our results show a 32% improvement in RMSE and a 36% improvement in convergence speed when compared to a baseline non pre trained model. 2023 IEEE.
- Source
- 2023 2nd International Conference for Innovation in Technology, INOCON 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep Learning; Machine Learning; Pre-training; Time Series Forecasting
- Coverage
- Khanna V., Christ (Deemed to Be University)Lavasa, Department of Data Science, Maharashtra, Pune, India; Joshua J., Christ (Deemed to Be University)Lavasa, Department of Data Science, Maharashtra, Pune, India; Pramila R.M., Christ (Deemed to Be University)Lavasa, Department of Data Science, Maharashtra, Pune, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835032092-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Khanna V.; Joshua J.; Pramila R.M., “Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19953.