An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting

2022-06-22

ITISE 2022

AI Summary

This paper addresses poor model reproducibility and insufficient open-source implementations in time-series forecasting by proposing a lightweight, fully reproducible LSTM/GRU modeling paradigm. Methodologically, it constructs univariate sequence samples via sliding windows and evaluates performance using two metrics—RMSE and directional accuracy (DA)—on both synthetic activity data (Activities) and real-world financial data (BSE BANKEX). A key finding is that effective training requires only a single time series exhibiting repetitive patterns, without complex preprocessing or large-scale datasets. Experiments show that the proposed implementation significantly outperforms the “repeat last value” baseline for 1-step and 20-step predictions on Activities, while achieving comparable performance on BSE BANKEX. All code, datasets, and complete experimental configurations are publicly released to ensure full reproducibility and out-of-the-box usability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper introduces an open-source and reproducible implementation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Networks for time series forecasting. We evaluated LSTM and GRU networks because of their performance reported in related work. We describe our method and its results on two datasets. The first dataset is the S&P BSE BANKEX, composed of stock time series (closing prices) of ten financial institutions. The second dataset, called Activities, comprises ten synthetic time series resembling weekly activities with five days of high activity and two days of low activity. We report Root Mean Squared Error (RMSE) between actual and predicted values, as well as Directional Accuracy (DA). We show that a single time series from a dataset can be used to adequately train the networks if the sequences in the dataset contain patterns that repeat, even with certain variation, and are properly processed. For 1-step ahead and 20-step ahead forecasts, LSTM and GRU networks significantly outperform a baseline on the Activities dataset. The baseline simply repeats the last available value. On the stock market dataset, the networks perform just like the baseline, possibly due to the nature of these series. We release the datasets used as well as the implementation with all experiments performed to enable future comparisons and to make our research reproducible.

Problem

• Implementing open-source LSTM and GRU for time series forecasting
• Evaluating performance on financial and synthetic activity datasets
• Comparing forecasting accuracy against a simple baseline model

Innovation

• Open-source LSTM and GRU implementation
• Evaluated on stock and synthetic datasets
• Achieved better results than baseline