Advancing delignification in the pulp and paper industry: Multivariate time series forecasting, explainability, and simulation analysis

Accurate rainfall forecasting is crucial in activities such as agricultural planning, water resource management, and flood preparedness, so failure to accurately forecast rainfall might undermine these activities. This study aimed to improve the accuracy of daily rainfall prediction in Hai district, Kilimanjaro region, Tanzania, a region characterised by a bimodal rainfall pattern, by comparing rainfall prediction accuracies of Traditional Machine Learning (TML) and Deep Learning (DL) models. The 25-year daily rainfall dataset for Hai district was first pre-processed by cleaning, normalisation, and splitting into training (first 60%), validation (next 20%), and test (last 20%) sets. TML models (K-Nearest Neighbours (KNN) through its variant KNN Regressor and Support Vector Machine (SVM) through its variant Support Vector Regressor (SVR)) were developed, trained, and their performances compared with developed and trained DL models (Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)). Each model’s test performance was evaluated using the Root Mean Squared Error (RMSE) metric. The findings revealed that the DL models, LSTM and GRU, achieved test RMSE scores of 6.35 mm and 6.49 mm, respectively, while the TML models, KNN and SVM, achieved test RMSE scores of 6.61 mm and 10.42 mm, respectively. The study demonstrated that DL models are more effective for accurate rainfall forecasting than TML models in regions with complex rainfall patterns. The insights gained will guide the development of more reliable, location-specific weather prediction systems, ultimately supporting informed decision-making in agriculture, environmental management, and flood risk reduction in Tanzania and other regions facing similar climatic challenges

Time series forecasting has been regarded as a key research problem in various fields. such as financial forecasting, traffic flow forecasting, medical monitoring, intrusion detection, anomaly detection, and air quality forecasting etc. In this paper, we propose a sequence-to-sequence deep learning framework for multivariate time series forecasting, which addresses the dynamic, spatial-temporal and nonlinear characteristics of multivariate time series data by LSTM based encoder-decoder architecture. Through the air quality multivariate time series forecasting experiments, we show that the proposed model has better forecasting performance than classic shallow learning and baseline deep learning models. And the predicted PM2.5 value can be well matched with the ground truth value under single timestep and multi-timestep forward forecasting conditions. The experiment results show that our model is capable of dealing with multivariate time series forecasting with satisfied accuracy.

Introduction: This study addresses the challenge of concept drift in multivariate time series (MTS) forecasting, where data distributions evolve, degrading model performance. The objective is to propose an adaptive hybrid model that combines Long Short-Term Memory (LSTM) networks with the ADaptive WINdowing (ADWIN) algorithm for effective drift detection and adaptive forecasting in real-time environments. Methods: The proposed ADWIN-LSTM framework integrates Bi-LSTM layers for temporal sequence modelling and ADWIN for monitoring prediction residuals to detect concept drift. A dynamic sliding window mechanism adjusts the training data scope based on drift type. Data preprocessing includes normalization, STL-based detrending, and PCA for dimensionality reduction. Hyperparameters are optimized using grid search. Results: Experiments on real-world and synthetic MTS datasets demonstrate that the proposed model outperforms baseline models (LSTM, GRU, CNN-LSTM, and ADWIN-RF) across RMSE, MAE, MAPE, and R² metrics. The model detects both abrupt and gradual drifts with minimal false positives and low detection delay (≤ 5 steps). Post-drift adaptation significantly improves forecasting accuracy. Discussion: The adaptive retraining strategy triggered by drift detection ensures computational efficiency and robustness in volatile environments. The dynamic integration of forecasting and drift detection enhances model adaptability to evolving data distributions. Conclusion: The ADWIN-LSTM framework effectively combines predictive learning and realtime drift adaptation, making it suitable for dynamic, high-stakes environments such as energy, traffic, and environmental systems. Future work includes online optimization and deployment on streaming platforms.

Multivariate time series forecasting is a crucial task that predicts the future states based on historical inputs. Although current deep learning-based methods have made significant advancements, they still face the criticism of lacking interpretability. The rise of the Kolmogorov-Arnold Network (KAN) provides a new perspective to implement an efficient and interpretable deep learning-based method for forecasting time series. However, we find there are two main challenges in the application of KAN in time series forecasting: how to select the appropriate one from various KAN variants and how to train the deep KAN-based network. To this end, we propose the multi-layer mixture-of-KAN network, which achieves excellent performance while retaining KAN's ability to be transformed into a combination of symbolic functions. The core module is the mixture-of-KAN layer, which uses a mixture-of-experts structure to assign variables to best-matched KAN experts. Then, we analyze the shortcomings of parameter initialization in the original KAN and provide an effective initialization method to alleviate training instability. Extensive experimental results demonstrate that our proposed method is effective in multivariate time series forecasting. Codes are released in https://github.com/2448845600/EasyTSF.

In the present work we extend the traditional C4.5 decision tree method for regression and forecasting of multivariate time series. In the proposed method, time series data is first fuzzified leading to a fuzzy time series (FTS) representation of the data. A fuzzy decision tree (FDT) based on C4.5 is employed to form the knowledge base of the FTS model. The method can deal with high-order and multivariate fuzzy time series, offering an explainable model. The FDT-FTS method is tested with data from IBOVESPA stock market index, which tracks the performance of around 50 most liquid stocks traded on the Sao Paulo Stock Exchange in Brazil. The method is applied to the IBOVESPA mini future contract time series in order to forecast future values using a mix of historical values and technical analysis indicators. This method is compared with Support Vector Regression (SVR) and Random Forest Regression (RFR), both methods implemented in the Scikit-Learn open-source library. The FDT-FTS model was implemented in Python programming language in the open-source pyFTS library. Although all three methods have similar performance, according to the MAPE, SMAPE, RMSE, NRMSE and MAE metrics, the proposed method is computationally faster and explainable.

Multivariate time series forecasting has very great practical significance for a long time, and it has been attracting the attention of researchers from a diverse range of fields. However, it is difficult to analyze the relationship and transformation law among multivariate data. Further, it is hard to obtain a relatively accurate prediction. In recent years, long short-term memory (LSTM) has shown high capability in dealing with nonlinearity and long memory of time series data. Although LSTM can also process multivariate data, it is insufficient to pay various degrees of attention to multivariate data. To address this issue, a multivariate time series prediction model based on multilayer perceptron (MLP), feedforward attention mechanism, and LSTM is proposed in this paper. Firstly, the simulation process utilizes the MLP module to map the multivariate initial sequences into another latent dimensional space, thereby obtaining easily captured mapping features. Then, these features are adaptively assigned attention weights through the feed-forward attention mechanism. Finally, the LSTM module uses these feature sequences with attention weights to make final predictions. The experimental results show that the method that combines the MLP layer with the feed-forward attention layer is effective in extracting multivariate features. Also, the empirical results indicate that our proposed framework (a combined model of MLP-Feedforward attention-LSTM) can achieve better performance than baselines.

Time series forecasting is often confronted with multivariate data, but few model is available in this situation. Besides, data distortion aggravates the difficulty to predict multivariate time series. To tackle such problems, we propose an approach based on convolutional neural network with a feature extraction layer added before convolution layer to extract multivariate features and handle multivariate time series data, as well as decreases the effect of distortion by transforming the sample into a denser representation with both its information and the information of its temporal neighbors. A full connection layer then fuses these extracted features and gets the final result. Given that events in the world are always related, using both the target time series and other related time series to forecast the future changes of the target dimension would achieve a better prediction. The proposed approach can process multivariate time series data and is robust to the number of samples, numeric ranges of data etc. Extensive experiments validate the effectiveness of the approach in accomplishing multivariate time series forecasting.

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

Chapter 3 - Role of laccase in the pulp and paper industry

One of the major problems of pollution control is scientific research, especially in the paper industry. The type of contamination resulting from industrial wastewater unsuitable and its very important for scientific background for researchers. Paper industries consume large volume of water, which usually contains a number of contaminants such as halogenated organic substances, lignin, etc. Biotechnology approach in the treatment of pollution sources is effective economically and environmentally. White fungus is one of the organisms that is given much attention. In the recent decades due to the inherent ability of the degradation of lignin in woody plants polymeric structures. This has received a good option for treatment of wastewater of paper industry. Fungus Phanerochaete Chrysosporium is a model of the lignin biodegradation studies and other biological materials for the development of enzyme production lignetic because it can produce a large collection of lignetic enzymes (manganese peroxides, lignin peroxides, lignin peroxides, laccase) can generate more than other species of fungi. This study aimed to investigate the impact of these fungi in the biological treatment of wastewater from paper and pulp industries; including waste reduction parameters: biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), total dissolved solids (TDS), turbidity and tree different pHs (pH 5, 6 and 7) at a temperature of 30°C. Results indicate 50% reduction of BOD, COD in the first three days of the test. Maximum reduction of wastewater parameters on the sixth day and occurred at pH6 and then fell on the ninth day of the test. This could be due to a lack of nutrients in the culture medium. Result in a temperature of 30° in pH6 after six days in 400 mg/l weight you can achieve optimal conditions for the purification. The results were 93%, 94%, 88%, 70%, and 83% respectively for BOD, COD, TSS, TDS and Turbidity. This method of wastewater treatment for pulp and paper industry is advisable. You can improve results and increase the efficiency of wastewater treatment fungi culture conditions by adding nutrients also examine the best conditions for growing white rot fungi upgraded.

Continual learning of multivariate time series (MTS) forecasting is critical for process industries where working condition drift is frequent due to the variation in the feed properties and other factors. However, existing continual learning methods struggle with dynamic input dimension changes, and the lack of symmetry-aware feature and dimension regulation further exacerbates the interference of irrelevant variables and dimensional inconsistency. To overcome this problem, G-MLoRA, a continual learning method based on dynamic redundancy clustering and multiple low-rank adapters, is proposed in this paper. This method can effectively enhance the network’s capability for prediction of multivariate time series under dynamic input dimensions. First, it groups MTS via Wasserstein distance-K-means clustering to reduce irrelevant variable interference. Second, each group is assigned to an exclusive LoRA adapter, with pre-trained backbone weights frozen during fine-tuning to lower complexity and mitigate catastrophic forgetting. Third, mini-batch gradient accumulation enables reuse of inconsistent-dimensional historical knowledge. Extensive experiments on two real grinding classification datasets show G-MLoRA outperforms baselines in new/historical knowledge compatibility, especially under dynamic dimensions.

Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of Things applications. Existing state-of-the-art robust forecasting models decompose time series into independent functions covering trends and periodicities. However, these independent functions fail to capture correlations among multiple time series, thereby reducing prediction accuracy. Moreover, existing robust forecasting models treat certain abrupt but normal changes, e.g., caused by holidays, as outliers because they occur infrequently and have data distributions that resemble those of outliers. This exacerbates model bias and reduces prediction accuracy. This paper aims to capture correlations across multiple time series and abrupt but normal changes, thereby improving prediction accuracy. We employ weak labels to partition the dataset into source and target domains. Then, we propose the Domain Adversarial Robust Forecaster (DARF). This forecasting model is based on adversarial domain adaptation and includes two novel modules: Correlated Robust Forecaster (CORF) and Domain Critic. Specifically, CORF constitutes an encoder-decoder framework proficient at robust multivariate time series forecasting, and Domain Critic works to reduce data bias. Extensive experiments and discussions show that DARF is capable of state-of-the-art forecasting accuracy.

Time series forecasting has witnessed a rapid proliferation of novel neural network approaches in recent times. However, performances in terms of benchmarking results are generally not consistent, and it is complicated to determine in which cases one approach fits better than another. Therefore, we propose adopting a data-centric perspective for benchmarking neural network architectures on time series forecasting by generating ad hoc synthetic datasets. In particular, we combine sinusoidal functions to synthesize univariate time series data for multi-input-multi-output prediction tasks. We compare the most popular architectures for time series, namely long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformers, and directly connect their performance with different controlled data characteristics, such as the sequence length, noise and frequency, and delay length. Our findings suggest that transformers are the best architecture for dealing with different delay lengths. In contrast, for different noise and frequency levels and different sequence lengths, LSTM is the best-performing architecture by a significant amount. Based on our insights, we derive recommendations which allow machine learning (ML) practitioners to decide which architecture to apply, given the dataset’s characteristics.

Chapter 8 - Hydrolytic xylanases production from fungi—An assessment for their production, properties and computational analysis of their gene sequences

Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) have gained significant popularity in time series forecasting across diverse domains including healthcare, astronomy, and engineering. However, the inherent variability in model performance due to random weight initialization raises questions about the reliability and consistency of these architectures for time series analysis. This study addresses this concern by conducting a comprehensive benchmark evaluation of nine neural network architectures: vanilla RNN, LSTM, GRU, and six hybrid configurations (RNN-LSTM, RNN-GRU, LSTM-RNN, GRU-RNN, LSTM-GRU, and GRU-LSTM). Performance evaluation was conducted using Monte Carlo simulation with 100 iterations across three real-world datasets: sunspot activity, Indonesian COVID-19 cases, and dissolved oxygen concentration measurements. Statistical analysis employed the Friedman test to assess performance differences across architectures. Results showed no statistically significant differences among the nine architectures. Despite the lack of statistical significance, consistent performance patterns emerged favoring LSTM-based hybrid architectures. The LSTM-GRU and LSTM-RNN configurations demonstrated superior performance across multiple evaluation metrics, with LSTM-RNN excelling in sunspot and dissolved oxygen forecasting, while standalone LSTM showed optimal performance for COVID-19 prediction. These findings provide evidence-based guidance for architecture selection in time series forecasting applications, suggesting that while statistical equivalence exists among architectures, LSTM-based hybrids offer practical advantages in terms of consistency and robustness across diverse temporal patterns.