Historical Time Series Data Research Articles

Improving access to mental health care: a system dynamics model of direct access to specialist care and accelerated specialist service capacity growth.

To simulate the impact on population mental health indicators of allowing people to book some Medicare-subsidised sessions with psychologists and other mental health care professionals without a referral (direct access), and of increasing the annual growth rate in specialist mental health care capacity (consultations). System dynamics model, calibrated using historical time series data from the Australian Bureau of Statistics, HealthStats NSW, the Australian Institute of Health and Welfare, and the Australian Early Development Census. Parameter values that could not be derived from these sources were estimated by constrained optimisation. New South Wales, 1 September 2021 - 1 September 2028. Projected mental health-related emergency department presentations, hospitalisations following self-harm, and deaths by suicide, both overall and for people aged 15-24 years. Direct access (for 10-50% of people requiring specialist mental health care) would lead to increases in the numbers of mental health-related emergency department presentations (0.33-1.68% of baseline), hospitalisations with self-harm (0.16-0.77%), and deaths by suicide (0.19-0.90%), as waiting times for consultations would increase, leading to disengagement and consequently to increases in adverse outcomes. Increasing the annual rate of growth of mental health service capacity (two- to fivefold) would reduce the frequency of all three outcomes; combining direct access to a proportion of services with increased growth in capacity achieved substantially greater gains than an increase in service capacity alone. A fivefold increase in the annual service growth rate would increase capacity by 71.6% by the end of 2028, compared with current projections; combined with direct access to 50% of mental health consultations, 26 616 emergency department presentations (3.6%), 1199 hospitalisations following self-harm (1.9%), and 158 deaths by suicide (2.1%) could be averted. The optimal combination of increased service capacitygrowth (fivefold) and direct access (50% of consultations) would have double the impact over seven years of accelerated capacity growth alone. Our model highlights the risks of implementing individual reforms without knowledge of their overall system effect.

Forecasting temperature of the Saudi Arabian Province of Makkah using a discrete state–space modeling approach

The maximum and minimum air temperature components (Tmax and Tmin) play a crucial role in science. This study proposes a discrete-time identified state-space modeling approach in which the temperature fluctuation was modelled as a state-space system with the temperature time series as inputs. We aim to provide a tool for projecting future scenarios of Tmax and Tmin. The current research employs a prediction-focused methodology to system identification, with the overarching goal of developing a realistic and dynamic system model. Data on the Tmin and Tmax recorded in the Saudi Arabian province of Makkah are used to test the accuracy and robustness of the proposed methodology. The proposed model was developed utilizing 120 years' (1901–2020) worth of historical monthly time series data on Tmax and Tmin. It was applied to anticipate future temperatures over the ensuing 60 years (up to 2080). For maximum temperature projections, the fit to the data or prediction focus was 87.04% and 85.14%, respectively for the identification (training) and validation phases of the model development. Akaike’s Final Prediction Error (FPE) and Mean Squared Error (MSE) values were observed to be 0.37 °C and 0.34 °C, respectively. The prediction focus during the identification and validation phases were 86.25% and 84.78%, respectively for the minimum temperature projection. The FPE and MSE values were 0.41 °C and 0.37 °C, respectively for this instance. The findings demonstrate that the recommended discrete state-space modeling approach may be utilized to predict temperature variations in the future.

A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.

Is entropy an indicator of port traffic predictability? The evidence from Chinese ports

Historical time-series data of container traffic in ports are characterised by strong randomness that causes the time-series prediction performance to vary significantly. In this sense, the prediction accuracy differs as a function of exogenous variables related to port characteristics and the maritime shipping context. Thus, the measuring problem of a priori container traffic predictability (i.e., the ability of methods to predict port traffic variables) arises. Academia suggests that this predictability may be related to entropy as a measure of the information provided. This paper analyses the Sample Entropy (SampEn), as an indicator of the complexity represented by the container traffic time-series data, comparing it with the error metrics provided by a statistical prediction and an artificial intelligent method. The Autoregressive Integrated Moving Average (ARIMA) and Back Propagation (BP) Neural Network are used to obtain the prediction performance of container port traffic in a data set composed of 20 Chinese ports and 5 selected international ports. Unexpectedly, the results indicate that the correlation between the SampEn and the prediction performance of the time series is weak. The complex ecosystem of the container shipping sector and the different port characteristics are suggested as the cause of this poor correspondence. Our findings help to understand the relationship between entropy and the predictability of time-series data in a sector (i.e. port traffic), which has not been explored before.

Nowcasting Hourly-Averaged Tilt Angles of Acceptance for Solar Collector Applications Using Machine Learning Models

Challenges in utilising fossil fuels for generating energy call for the adoption of renewable energy sources. This study focuses on modelling and nowcasting optimal tilt angle(s) of solar energy harnessing using historical time series data collected from one of South Africa’s radiometric stations, USAid Venda station in Limpopo Province. In the study, we compared random forest (RF), K-nearest neighbours (KNN), and long short-term memory (LSTM) in nowcasting of optimum tilt angle. Gradient boosting (GB) is used as the benchmark model to compare the model’s predictive accuracy. The performance measures of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and R2 were used, and the results showed LSTM to have the best performance in nowcasting optimum tilt angle compared to other models, followed by the RF and GB, whereas KNN was the worst-performing model.

Research on Gas Concentration Prediction Based on the ARIMA-LSTM Combination Model

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.

A Deep Stacked Bidirectional LSTM (SBiLSTM) Model for Petroleum Production Forecasting

Deep learning models have demonstrated outstanding forecasting effects and are extensively used in forecasting problems in numerous scenarios. The Long short-term memory neural network (LSTM), a deep learning variant, has huge potential in forecasting the forthcoming values of given patterns using historical time series data. Lately, the forecasting task has captivated the focus of researchers in deep learning areas to handle the constraints of traditional statistical approaches. Owing to the dire need for accurate forecasting and also due to technological advancements, the collection of time series data has become more accessible, and this has paved the way for deep forecasting models. In this paper, we propose the Stacked Bi-LSTM (SBiLSTM) architecture, an adaptation of the traditional deep long-short term memory (TDLM). The approach is evaluated using two oilfield production time series. The performance of the proposed SBiLSTM model is compared with the recurrent neural network-RNNs, multi-layer RNNs, deep gated recurrent unit (DGRU), and deep long-short term memory (DLSTM). Using different measurement criteria, the empirical results show that the proposed DLSTM model outperforms other standard approaches. It is observed that the proposed SBiLSTM model acquires long and short-range interdependent features of univariate time series data without the large memory requirement.

Time Series Forecasting Fusion Network Model Based on Prophet and營mproved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou, Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them. This paper proposes a P-gLSTNet model, and uses Autoregressive Integrated Moving Average model (ARIMA), long and short-term memory (LSTM), and Prophet to predict and compare the emissions in the future period. The experiments are validated on four public data sets and one self-collected data set, and the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are selected as the evaluation metrics. The experimental results show that the proposed P-gLSTNet fusion model predicts less error, outperforms the backbone method, and is more suitable for the prediction of time-series data in this scenario.

Improving Net Energy Metering (NEM) Actual Load Prediction Accuracy using an Adaptive Learning Rate LSTM Model for Residential Use Case

As an effort to promote renewable energy-based power generation, one of Malaysia’s initiatives is the net-energy metering (NEM) scheme. One of the shortcomings of residential Photovoltaic (PV) systems under the NEM scheme is that it operates with smart meters only whereby the actual load profiles by the residential consumers remain unknown. Accurate load prediction for NEM consumers is crucial for optimizing energy consumption and effectively managing net metering credits. This study proposes a new model that incorporates an adaptive learning rate and Long Short-Term Memory (LSTM) to predict the solar output power that subsequently predicts the actual load used by the NEM residential consumers. The proposed model is trained and tested using historical time series data of projected PV power and weather conditions, considering the GPS location of the PV system. The outcome of the proposed model is then compared with other state-of-the-art models like ARIMA and regression methods. It is shown that the proposed model outperforms the traditional forecasting models with a Root Mean Square Error (RMSE) value of 0.1942.

Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

The widespread use of distributed energy sources (DERs) raises significant challenges for power system design, planning, and operation, leading to wide adaptation of tools on hosting capacity analysis (HCA). Traditional HCA methods conduct extensive power flow analysis. Due to the computation burden, these time-consuming methods fail to provide online hosting capacity (HC) in large distribution systems. To solve the problem, we first propose a deep learning-based problem formulation for HCA, which conducts offline training and determines HC in real time. The used learning model, long short-term memory (LSTM), implements historical time-series data to capture periodical patterns in distribution systems. However, directly applying LSTMs suffers from low accuracy due to the lack of consideration on spatial information, where location information like feeder topology is critical in nodal HCA. Therefore, we modify the forget gate function to dual forget gates, to capture the spatial correlation within the grid. Such a design turns the LSTM into the Spatial-Temporal LSTM (ST-LSTM). Moreover, as voltage violations are the most vital constraints in HCA, we design a voltage sensitivity gate to increase accuracy further. The results of LSTMs and ST-LSTMs on feeders, such as IEEE 34-, 123-bus feeders, and utility feeders, validate our designs.

Online state-of-charge estimation refining method for battery energy storage system using historical operating data

In battery energy storage systems (BESS), state-of-charge (SoC) is of great significance to optimize the charge and discharge schedules. Some existing SoC estimators implemented in battery management system (BMS) of BESS may suffer from significant error, which will cause permanent damage to service life or economic loss. This paper identifies the causes of inaccurate SoC in the practical BESS and confirms the result with laboratory test. On this basis, an online method based on historical operating data is proposed to refine real-time SoC estimation from BMS. In the proposed refining method, SoC reference points are initially located from historical time-series data and the maximum available capacity of charge or discharge are further determined with a weighted least squares fitting. Finally, refined SoC estimation result can be determined by enhanced coulomb counting method. The experimental results based on laboratory test data and operation data from a practical BESS prove that the proposed SoC refining approach can effectively provide more accurate estimation.

Trend analysis and forecasting of streamflow using random forest in the Punarbhaba River basin.

Streamflow rate changes due to damming are hydro-ecologically sensitive in present and future times. Very less studies have done an investigation of the damming effect on the streamflow along with future forecasting, which can be the solution for the existing problems. Therefore, this study aims to use the Pettitt test as well as standard normal homogeneity test (SNHT) to discover trends in streamflow with the future situation in the Punarbhaba River in Indo-Bangladesh from 1978 to 2017. Trend was spotted using Mann-Kendall test, Spearman's rank correlation approach, innovative trend analysis, and a linear regression model. The current work additionally uses advanced machine learning techniques like random forest (RF) to estimate flow regimes using historical time series data. 1992 appears to be a yard mark in this continuum of time series datasets, indicating a significant transformation in the streamflow regime. The MK test as well as Spearman's rho was used to find a significant negative trend for the average (-0.57), maximum (-0.62), and minimum (-0.48) flow regimes. The consistency of the flow regime has been losing consistency, and the variability of flow regime has increased from 2.1 to 6.7% of the average water level, 1.5 to 6.5% of the maximum streamflow, and 3.1 to 5.8% of the minimum streamflow in the post-change point phase. The forecast trend using random forest for streamflow up to 2030 are negative for all four seasons with a flow volume likely to be reduced by 0.67% to-5.23%. Annual and monthly streamflows revealed very negative tendencies, according to the conclusions of unique trend analysis. Flow declination of this magnitude impacts downstream habitat and environment. According to future estimates, the seasonal flow will decrease. Furthermore, the outcome of this research will give a wealth of data for river management and other places with comparable environment.

Applications of System Dynamics and Big Data to Oil and Gas Production Dynamics in the Permian Basin

In this paper, the authors create, justify, and document a system dynamics model of the oil and gas production within the Permian Basin of Texas. Then the researchers show how to fit the model to historical time series data (big data). The authors use the model to better understand the process structure, the production dynamics, and to explore the deleterious consequences of limited pipeline capacity in the Permian Basin. The model is also employed to better understand how to increase revenues derived from the basin. From this model, numerous suggestions are made as to how to improve the overall revenue and profitability coming from the Permian Basin. The model's ultimate purposes and its associated big data are to foster a basic appreciation of the causality inherent in the ‘system' and how basic model parameters affect and influence measures of model performance.

Efficient data replay mechanism of sensor stream data based on concurrent buffer pool

Analyzing historical time series data in the form of replay is very important. When replaying massive sensor data, long processing times predominantly occur, that significantly affects the performance of the whole system. Existing solutions do not consider the requirements of large-scale sensor data replay. Therefore, a data replay mechanism based on a concurrent buffer pool is proposed. In this mechanism, a multi-level buffer queue is designed and implemented using a priority queue to ensure the efficiency and stability during the replaying process. Then, a task queue is adopted to ensure the correct order when multiple threads fill the multi-level buffer queue. Moreover, an anomaly detection operator is integrated to reduce outliers in replay data. Using the real hydrologic dataset, the data replay mechanism is evaluated by several experiments. The results show that the data replay mechanism has better performance compare to Apache Phoenix and Apache Hive.

Feasibility Study on the Influence of Data Partition Strategies on Ensemble Deep Learning: The Case of Forecasting Power Generation in South Korea

Ensemble deep learning methods have demonstrated significant improvements in forecasting the solar panel power generation using historical time-series data. Although many studies have used ensemble deep learning methods with various data partitioning strategies, most have only focused on improving the predictive methods by associating several different models or combining hyperparameters and interactions. In this study, we contend that we can enhance the precision of power generation forecasting by identifying a suitable data partition strategy and establishing the ideal number of partitions and subset sizes. Thus, we propose a feasibility study of the influence of data partition strategies on ensemble deep learning. We selected five time-series data partitioning strategies—window, shuffle, pyramid, vertical, and seasonal—that allow us to identify different characteristics and features in the time-series data. We conducted various experiments on two sources of solar panel datasets collected in Seoul and Gyeongju, South Korea. Additionally, LSTM-based bagging ensemble models were applied to combine the advantages of several single LSTM models. The experimental results reveal that the data partition strategies positively influence the forecasting of power generation. Specifically, the results demonstrate that ensemble models with data partition strategies outperform single LSTM models by approximately 4–11% in terms of the coefficient of determination (R2) score.

Persistence of nitrogen oxides emissions using historical time series data: evidence from 37 countries

ABSTRACT Two features in time series data: the existence of time trends and the degree of persistence, are examined in this work on the nitrogen oxides emissions from 37 OECD countries. Updated techniques in time series are used that allow for fractional degrees of differentiation in the data. Thus, if the number of differences required is one, nitrogen oxides emissions are not mean reverting in the sense that if there is an exogenous shock (resulting from a technological advancement to change nitrogen oxides emissions), the effect of such shock on nitrogen oxides emissions will be permanent. Time trends are observed in half of the series. For these countries the trend coefficient is found to be positive in all cases. This is an indication that continuous technological progress is needed in taming NOx emissions. In addition to developing their own local technologies, less technologically endowed OECD countries should engage in collaboration with the more technologically endowed countries in order to facilitate increase in trans-border transfer of technology. The technologically advanced countries should also strive to continue to introduce better technologies in a bid to reduce NOx emissions. Most of the results show evidence for persistence of nitrogen oxides emissions.

Time Series Analysis of Monthly Coffee (Robusta) Prices in India using Box-Jenkins Approach

Robusta coffee is a type of coffee made from the Coffea canephora plant's beans (seeds). It is the world's second most popular coffee, accounting for 43% of global coffee production with arabica constituting the remainder except for the 1.5% constituted by Coffea liberica. The purpose of this study is to focus on predicting monthly coffee prices in India by using the historic time series data. The objective of this paper is to fit an Autoregressive Integrated Moving Average model using Box-Jenkins approach. Numerous fields, including agricultural production, animal husbandry and dairy economics, stock price prediction, etc. depend heavily on forecasting. To choose the best model, Autoregressive (AR), Moving Average (MA), and Autoregressive Integrated Moving Average (ARIMA) processes was used to select the best model for monthly coffee prices in India. This paper discusses ARIMA (p, d, q) time series analysis and its components, ACF, PACF, Normalized BIC, Box-Ljung Q Statistics, and Residual analysis. According to the best fitted model i.e., ARIMA (0,2,1) monthly coffee prices in India is expected to increase to INR 89.35 kg-1 in the month of November 2022. The outcomes are represented numerically and graphically.

A novel approach to incorporate investor’s preference in fuzzy multi-objective portfolio selection problem using credibility measure

Investment decision making is usually a multi-objective optimization problem in an uncertain environment. In a real-life scenario, an investor aims to choose a portfolio based on his/her preferences for each objective. Credibility theory has been attributed as one of the most effective ways to model uncertain portfolio attributes. LR fuzzy numbers based credibilistic moments have been used successfully in the recent past to model portfolio selection problems in uncertain environments. Traditionally, the preferences on portfolio attributes are reflected as user defined threshold values. This work proposes a novel way to integrate the investor’s preferences in the portfolio selection model. Investor’s preferences are specified as LR fuzzy numbers corresponding to the portfolio’s expected return and illiquidity distributions. The portfolio models formulated in this study attempt to attain them to the best possible extent. Four new models are presented considering uncertain portfolio attributes, namely, fuzzy return and fuzzy illiquidity. The models are solved using the MIBEX-SM Genetic Algorithm (GA) and ECE methodology. In order to demonstrate the efficient working of the proposed models, practical applications of portfolio selection problem using historical time series data of National Stock Exchange (NSE) is presented. Credibilistic Sharpe Ratio (CSR) has been used to compare the efficiency of models among themselves as well as with the Nifty−50 index.

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Objectives: Atmospheric Ozone plays an important role in global climate change, human health and environmental conditions. Accurate and timely prediction of variation in Total Column Ozone (TCO) concentration is very important for both climatology and environment. In this context, the present study aims to utilize the advancement of Artificial Intelligence in Machine learning and a conventional method to develop models for the prediction of TCO concentration using historical time series data over a tropical region, India. Methods: Long Short-Term Memory (LSTM) deep learning networks are very useful in classifying, processing and making predictions based on time series data. In this work, Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM) models are developed for forecasting TCO over a tropical region. The Statistical Parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to analyze and evaluate the performance of the proposed models. Findings: In the present study the TCO concentration varied between 217.4 DU and 288.6 DU. The performances of MLR and LSTM model were compared. From the results, it is evident that the values of RMSE, MAE and MAPE of the MLR model are 3.807924, 2.93766, 1.15 respectively whereas the values for LSTM model are 3.074492, 2.574, 1.01 respectively. LSTM was found to be the accurate model for the study data set and the predicted TCO concentration has a very good correlation with the actual observations and it does not rely on the meteorological parameters. Novelty: The LSTM models are usually employed in the field of machine translation, speech recognition and for the prediction of traffic levels, stock levels and air pollutants. This work utilizes the fast numerical machine learning computing library Tensorflow and a high level neural network library Keras that runs on top of Tensorflow to develop a LSTM network model for the prediction of the concentration levels of TCO. Keywords: Total Column Ozone; Regression; Forecasting; Climate Change; Long Short-Term Memory

A Hybrid ARIMA-GABP Model for Predicting Sea Surface Temperature

Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST.