Time Series Techniques Research Articles

Autocorrelation and Cross-Correlation of Climatological Time Series: A Bibliometric Analysis

Objective: This article aims to contribute to the investigation of the evolution of scientific knowledge related to autocorrelation and cross-correlation analysis techniques in time series of climatological variables, wind speed and solar radiation, in order to understand what is discussed on the subject and, in addition, to support future productions. Theoretical Framework: Conducting a study to investigate trends in the evolution of publications related to autocorrelation and cross-correlation measures, used to analyze climatological time series, is relevant to consolidate and deepen knowledge in this scientific field. Understanding the behavior of climatic elements is relevant in several areas of application: the spread of respiratory diseases; agricultural and urban planning tools; the relationship between climatological factors and pollutants; and renewable energy sources. Method: To achieve this objective, the bibliometric review technique was used to map the literature on the research topic, assessing the impact of production by country, identifying the authors, institutions and journals that stand out, trends in the number of publications and microanalysis. Results and Discussion: A total of 173 scientific articles were recorded during the analysis period, with the highest productivity peaks during the years 2013 and 2019 with 12 and 13 publications, respectively, representing together 18.25% of the total publications and from these years onwards a significant growth in the number of publications was evidenced. The countries that stood out the most in number of publications were China (21.96%), followed by the United States (4.05%) and Japan (4.05%). The most relevant authors were Li, Y. in the lead with 5 articles and Dinpashoh, Y. with 4, the same authors maintained productions on the subject over time. Conclusion: Our findings highlighted the relevance of mapping scientific advances related to autocorrelation and cross-correlation of climatological time series. It is noted that China is the country with the highest number of citations received and its significant number of articles and citations may indicate that this country has relevant scientific publications on the subject. These results will be useful as a constructive reference for future research on the subject.

Advancing Financial Forecasts: Stock Price Prediction Based on Time Series and Machine Learning Techniques

ABSTRACT Since the beginning of stock trading, investors and researchers have tried to find effective ways to predict the direction of stock prices on the next day. However, predicting stock prices is a hard task because there exist many factors that may affect the next day’s stock prices. Recently, investors and researchers have adopted machine learning techniques with technical indicator analysis to make prediction. But the prediction accuracy is unsatisfactory. Thus, this study aims to examine the problem of stock price prediction with time series and proposes an effective way to filter out the datasets, which consists of three key steps: First, a time series model with long-short-term memory (LSTM) was used to identify the possible problems and solve them in the process of stock price prediction. Second, the results of LSTM were adopted to influence the stock trend prediction by a variety of machine learning techniques, including random forest, support vector machine, light gradient boosting machine (LightGBM). Third, a novel method was proposed to select suitable datasets through buying or selling. Hereby, FTSE TWSE Taiwan 50 Index stocks were used as training and testing datasets, respectively. Some important days were selected for prediction and decision making. The experimental results show that the highest prediction accuracy of 86% and the average prediction accuracy of 82% have been obtained. Consequently, when comparing to other existing methods, the accuracy of predicting stock price trend with our proposed approach has been significantly improved. Reference No.: 2024-UAAI-0010R1/243667820 Subject Area: Intelligent Investment System

Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services

To maximize the business value of the cloud, the cost of cloud solutions is explored alongside technical quality and performance. To enable this form of exploration engineering, finance and business teams collaborate in the context of FinOps, the operational framework that provides the required decision-making. Prominent providers, such as Google and Microsoft, provide FinOps to their customers, integrating cost factors when designing a cloud solution. However, different providers apply different pricing policies for their products, and these policies also change through time. Therefore, there are numerous efforts to explore price evolution through time for different cloud products applying different decision-making methods using different datasets. In an effort to establish a systematic approach to support decision-making on alternative pricing policies for cloud services and compare them across services and providers, the CloudPricingOps framework is proposed in this paper. It constitutes a decision support system that provides alternative decision-making methods, such as hedonic models, time series, and clustering and machine learning techniques, to deal with problems related to cloud product pricing policy analysis, comparison and prediction. It also constitutes a systematic method of discrete steps to integrate additional decision-making methods to deal with these problems and input datasets to be used regardless of the method or the cloud pricing problem that needs to be solved. Two discrete examples based on real data are also presented in this paper to demonstrate the usefulness of the CloudPricingOps framework for cloud engineering and business teams.

Credit expansion and economic growth in India: empirical evidence

Expansion in credit is often considered as catalyst to economic growth. However, several studies dispute such relationship and argue that the expansion of credit may not always accelerates economic growth. Using national and state level data, we examine the association between expansion of bank credit and economic growth in India. We apply time series and panel data econometric techniques to examine the linkage between credit expansion and economic growth in India. Our findings suggest that at national level, increase in credit-GDP ratio decelerates economic growth. While using state level data, we observe a positive association between credit-NSDP ratio and the NSDP growth rate among the states with lower growth rate. Whereas the relation is found to be negative among the states with comparatively higher growth rate. Our study conclude that generalised policy of credit expansion may not be suitable to accelerate growth across states with different levels of economic growth.

Time Series Modelling and Prediction of WTI Crude Oil Prices Trend using Machine Learning Techniques

It is challenging to forecast crude oil prices. Given that the international crude oil price is time-varying and nonlinear, suggest a new mixed method for oil price forecasting. First, the traditional time series technique ARIMA method for predictions is incorporated. Then, The time-varying and nonlinear components of crude oil prices are forecasted using machine learning approaches such as boosted trees, SVM, ANN, LSTM, bagged trees and linear regression, etc. The ultimate predicted outcomes of crude oil prices are then calculated by adding the predicted prices of each component. The results showed that the new proposed hybrid method was capable of a high degree of oil price prediction based on its exceptional ability to adapt sample selection, data frequencies and structural breaks. In addition, the comparisons show that the new method proves to be more accurate than these well-known methods for crude oil price forecasts. The WTI daily data from 1986 to 2018 has been utilised for forecasting of crude oil trend for 2018 in next 50 days. Forecasts were made using the time series forecasting approach with the ARIMA model and the machine learning approach. Validation metrics have been used to evaluate the performance of these models and it was noted that they performed well. The effects of prediction methods on crude oil data are discussed with the help of graphs for the predictions for next 50 days, actual and predicted plots, model performance plot, and validation performance plot for all the methods. Compared to the previous published work, the accuracy of the problem has been verified by comparing it to the quantitative outputs exhibited in this, and the agreement between the results is excellent, which has led to confidence in the quantitative results projected in this study.. KEYWORDS :Crude oil price, ARIMA model, Time series forecasting, Machine learning techniques, Error metrics.

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

USO DE MODELOS DE PREDIÇÃO PARA MELHORAR O PLANEJAMENTO DE ESTOQUE DE ALTA ROTAÇÃO - ESTUDO EM UMA EMPRESA NO SETOR DE BEBIDAS

This project develops a prediction model for inventory planning of high-turnover products, specifically draft beer, in a company in the beverage sector. Using neural network and time series techniques, the aim is to minimize waste and guarantee product availability, taking into account the short shelf life of draft beer and the need for advance orders. The research involved collecting sales data from previous years, processing the data and developing a user-friendly interface. The model was trained with machine learning algorithms to predict weekly sales. The results show that the model is effective at predicting sales patterns, allowing for adjustments and future integrations, such as the inclusion of climate and demographic data. This work provides a solid basis for optimizing inventory management and formulating more efficient commercial strategies.

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Fusion of Image Transformation Techniques for IoT-based Multivariate Time-Series

The emergence of the Internet of Things (IoT) has ushered in a new era of data generation, with the opportunity for data to become a key element of connected devices. This study investigates new methods to bridge the realms of multivariate time-series data and image analysis, paying special attention to Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP) transformation techniques. These techniques serve to convert raw time-series data into visual representations, laying the foundation for deeper analysis and predictive modeling. The study introduces a novel paradigm by not only employing individual image transformation techniques but also fusing them in both horizontal and square orientations. By leveraging Convolutional Neural Networks (CNNs), this study demonstrates the efficiency of innovative fused oriented image transformation techniques in predicting complex patterns within a multivariate time-series dataset related to electricity distribution and transformer oil temperature. Experimental results demonstrate that the fusion of image transformation techniques yields notably enhanced performance compared to employing each technique individually. Fused-Horizontal techniques exhibit the highest performance, while Fused-Square techniques show the second best performance among all fused oriented and individual techniques. This emphasizes the importance of fused oriented image transformation techniques in prediction tasks.

Forecasting stock market returns using deep learning and time series techniques: a comparative and empirical study using technical indicators

Forecasting stock market returns using deep learning and time series techniques: a comparative and empirical study using technical indicators

Forecasting of Electricity Consumption by Seasonal Autoregressive Integrated Moving Average Model in Assam, India

Sustainable electricity consumption, which is about balancing economic growth, social development and environmental protection are the core principles of the Sustainable Development Goal (SDG). Accurate forecasting of electricity consumption is very important for attaining SDG - 7 which aims to ensure access to affordable, reliable, sustainable and modern energy for all. Through this paper, attempt is made to forecast monthly electricity consumption in Assam. For this purpose, one of the most widely used time series techniques, viz., Seasonal Autoregressive Integrated Moving Average (SARIMA) is applied by considering the time period April, 2013 to February, 2023 to study the seasonal influence on the electricity consumption in Assam. By applying Augmented Dickey Fuller test, it is observed that the data series becomes stationary at first order difference and the result of Canova Hansen test reveals that no seasonal differencing is required for our considering time period. SARIMA (1,1,1) (1,0,1)12 has been selected for forecasting purpose by following the results of Akaike Information Criterion. By analyzing the model statistics, residual ACF and PACF plots of the selected model, it is found that SARIMA (1,1,1) (1,0,1)12 can be effectively recommended for forecasting of monthly electricity consumption in Assam with Mean Absolute Percentage Error as 3.12%.

Functional Time Series Models for K-Step Prediction of Sugar Production: A Case Study of Eswatini, Kenya, and South Africa

Sugar, produced from cane or beet, is a vital energy source and a globally traded commodity. Most business happens in a futures exchange market where speculators, hedgers, and institutional consumers and producers, make decisions based on their understanding of the future supply and demand situation. Sugar is sometimes bought and sold before the cane or beet is planted. Hence, improving sugar production forecast accuracy is vital to maximize gains and enable effective planning. The study considered the annual sugar production total as a function of the monthly output rather than a scalar quantity and analyzed historical data using three functional time series techniques. In particular, the methods used for k-steps and dynamic updating prediction of sugar production include Local Polynomial Regression (LPR), Ridge Regression (RR), and Penalized Least Squares (PLS). The performance of the three models was compared to that of the automatic ARIMA method using the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) measures to establish the suitability of each technique in sugar production forecasting. The LPR, RR, and PLS outperformed the ARIMA method in all three cases. However, the prediction accuracy was lower for highly volatile datasets from countries that overly depend on rainfall for cane development.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease’s transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country’s epidemic pattern and assist the government in developing better effective interventions.

A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Context: Epilepsy is a neurological disease that affects more than 50 million people worldwide, causing recurrent seizures, with a significant impact on patients' quality of life due to abnormally synchronized neuronal activity. Method: This article discusses three methods used for signal analysis in patients diagnosed with epilepsy. Conventional signal decomposition methods, such as the fast Fourier transform, widely used in signal analysis based on time series techniques, have some issues when analyzing nonlinear and non-stationary signals, in addition to difficulties in detecting low-order frequencies. Results: To overcome these limitations, alternatives such as empirical mode decomposition and one of its variants, called ensemble empirical mode decomposition, have been developed. These techniques allow observing different oscillation modes through intrinsic mode functions and instantaneous frequencies. Conclusions: In this study, the results obtained through the aforementioned techniques were compared, revealing the impact of nonlinear methods on the reconstruction of brain activity.

Modelling and forecasting Masvingo Province maternal mortality using time series models

In light of the Zimbabwean government's efforts, particularly through the Ministry of Health and Child Care (MoHCC), to reduce maternal mortality rates and achieve the aim of Sustainable Development Goal (SDG) number three, target one (SDG 3.1), which aims to reduce the global maternal mortality rate to fewer than 70 deaths per 100,000 live births, maternal mortality rates in Zimbabwe continue to rise. Time series techniques were used to model and predict quarterly maternal mortality statistics for Masvingo Province from January 2014 to December 2021. The time plot analysis revealed significant fluctuations in mortality, with the highest rates of maternal deaths recorded in 2018. The application of the Box-Jenkins methodology identified the ARIMA(2, 1, 1) model as the most suitable for modeling and forecasting quarterly maternal deaths among the fitted models. The suitability of the model was validated by the Akaike Information Criterion (AIC), and its forecast accuracy was confirmed by the Mean Absolute Error (MAE) and the root mean square error (RMSE). Consequently, it was used to project future maternal deaths. The projected values indicate a slight increase in quarterly mortality rates during the period, but there appears to be a relatively stable trend with moderate fluctuations, suggesting that Zimbabwe has not met the targets of SDG 3.1. These results underscore the need to re-assess current intervention programs aimed at reducing maternal mortality. The findings of the study could guide the refinement of existing strategies and the implementation of innovative solutions to urgently address unacceptably high mortality rates. Using statistical models such as the one used in this study, the Ministry of Health and Child Care (MoHCC) can make informed decisions in the health sector and implement effective interventions to combat maternal mortality.

Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

Acumulación regulatoria y crecimiento económico

Nowadays, there is no consensus on the effects of regulation on economic growth. In this document, we analyze the relationship between regulation and economic growth for Colombia, using data on the accumulation of regulation and real GDP during the 1991 - 2019 period for 9 large productive sectors of the Colombian economy. Different models are estimated using time series and panel data techniques to present evidence at the economic sector level. The results show that, for some sectors, such as agriculture, forestry, fishing, mining, manufacturing, electricity, gas and water, commerce, restaurants and hotels, financial and business services and community, social and personal services, there exists an inverted U relationship between regulatory accumulation and GDP. These results offer important lessons for those responsible for the design and formulation of public policies on matters of regulatory improvement.

Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors

In this paper, we explore the possibility of exploiting retrospective clinical data from Electronic Health Records (EHR) for classification tasks in chronic patients. The different intervals, short length and high class imbalance make it unfeasible to use traditional time series techniques. The first contribution of the paper is a preprocessing method to construct a multivariate time series dataset using EHR data, which infers missing data and regularizes the data frequency. The second contribution addresses class imbalance by using domain knowledge and existing short EHR series. We synthetically extrapolate patients’ data by using similar long time series and a fuzzy-based approach. The paper addresses the problem of detection of Diabetic Retinopathy (DR). Expert domain knowledge from ophthalmologists has been used in the proposed techniques to guide the processing of time series. The novelty in that case study consists in not using eye-fundus image analysis. Instead, the proposed methods are based solely on EHR data. Several multivariate multiclass time series classifiers are used to detect the four levels of DR severity from the pre-processed data sequences. Experiments prove the quality of the sequence preprocessing techniques proposed for EHR data. Results indicate that the TapNet classifier is the best one for DR grading. Despite being tested for DR detection, the proposed data preparation methods are applicable to other diseases with similar characteristics.

Modeling and forecasting the volatility of some industry development indicators in Ethiopia using multivariate GARCH models

Industry development indicators refer to a set of measures used to assess the performance and growth of industries. The main aim of this study was to assess the relationship between industry development indicators in Ethiopia using a multivariate GARCH model based on World Bank data from 1982 to 2021. A time series technique using annual data for the period 1982–2021 is utilized, and multivariate generalized autoregressive conditional heteroscedasticity was performed for volatility modeling. The results of the diagonal BEKK (1, 1)-GARCH model showed that there is strong evidence for a GARCH effect and the presence of a weaker ARCH effect, Equations show a statistically significant co-variation in shocks, which depends more on their lags than on past errors. Consequently, development indicator shocks are influenced by past information. The cross-volatility effects are higher than the own-volatility effects in Industry GDP, manufacturing GDP, and manufacturing exports. However, past volatility shocks in industry growth have less effect on cross-volatility than its own volatility shock. The implication of the study is that both domestic policymakers and development partners should support and motivate the growth of manufacturing sectors and manufacturing exports, since this is a necessary condition for promoting industry growth.

Sales Forecasting and Inventory Optimization at Big Mart using Time Series Technique: A Survey

Sales Forecasting and Inventory Optimization at Big Mart using Time Series Technique: A Survey