Time Series Analysis Research Articles

Understanding changes of laser backscattering imaging parameters through the kiwifruit softening process using time series analysis

ABSTRACT During kiwifruit storage, quality monitoring is required for inventory planning and consistent quality maintenance. Commercial near-infrared (NIR) spectrophotometers showed reduced performance in the estimation of kiwifruit flesh firmness (FF) as the FF estimation is indirect and can be affected when both, textural structures and absorbing compounds, change during postharvest ripening. Laser backscattering imaging (LBI) records the backscattered signal after a single laser beam interacts with kiwifruit tissue, including merged information on light absorption and scattering. Measurements were carried out at 830 nm, where scattering is most dominant. In this work, time series of kiwifruit ‘Zesy002’ (n = 30) and ‘Hayward’ (n = 30) LBI were collected through the postharvest ripening during a 15-day shelf life at 20°C. Four LBI parameters capturing DIP, FWHM, SLP and HWQM were selected in this study. ‘‘Zesy002’ DIP, FWHM, SLP, and HWQM increased approx. 0.6 cm, 0.2 cm, 0.3 and 0.14 cm, respectively. ‘Hayward’ LBI increased approx. 0.2 cm, 0.1 cm, 0.2 and 0.04 cm, respectively. Different initial LBI values between cultivars and LBI changes may reflect the actual stage of softening, affected by kiwifruit ripeness. In conclusion, time series analysis could be useful in describing kiwifruit LBI change during ripening and making forecasts.

Code Crimson: A Postpartum Hemorrhage Bundled Intervention Quality Improvement Project.

Postpartum hemorrhage (PPH) is a leading cause of maternity mortality in the United States. The Code Crimson project aimed to enhance PPH management by implementing a standardized intervention bundle to mitigate morbidity and mortality associated with PPH. At a large Philadelphia tertiary hospital, health disparities existed for severe maternal morbidity and mortality, and PPH was a significant factor. A quality improvement design, using Plan-Do-Study-Act cycles and interrupted time series analysis, was undertaken. The Code Crimson project implemented a standardized bundle to manage PPH, including blood product administration and massive transfusion protocol activation. After implementing the Code Crimson bundle, there was a significant decrease in blood product use (P <.001), with minor reductions in packed red blood cell administration over 4 units and mean blood loss. The Code Crimson bundle effectively reduced blood product utilization for PPH treatment.

The impact of the COVID-19 pandemic on calls to an alcohol and drug helpline in Victoria, Australia, for alcohol, methamphetamine and cannabis use concerns.

Helplines are often the first contact with the alcohol and other drug (AOD) treatment system. We examined call data from an AOD helpline in Victoria, Australia, to explore the association between COVID-19 lockdown measures and frequency of calls. This was a retrospective analysis of AOD helpline data collected between January 2018 and September 2020, for alcohol, methamphetamine and cannabis use concerns. Linear and logistic regression analyses examined differences in pre-COVID-19 (January 2018 to March 2020) and during COVID-19 (March 2020 to September 2020) caller characteristics, and interrupted time-series analyses examined changes in frequency of calls relative to lockdown measures. There were 14,340 calls for alcohol (n = 10,196, 71.10%), methamphetamine (n = 2522, 17.59%) and cannabis (n = 1622, 11.31%). Relative to pre-COVID-19, during COVID-19 there was an increase in the rate of change over time in number of alcohol calls (b = 0.39), increase in first-time alcohol callers (OR = 1.29), and reduction in first-time methamphetamine callers (OR = 0.80). During COVID-19, alcohol callers had lower Socio-Economic Indicators for Areas scores (b = -3.06) and cannabis callers were younger (b = -2.07). During COVID-19, there were reductions in alcohol calls involving counselling/support (OR = 0.87) and information provision (OR = 0.87), cannabis calls involving information provision (OR = 0.71) and methamphetamine calls involving referral (OR = 0.80). In the first 6 months of the pandemic, frequency of alcohol-related calls increased over time, and first-time alcohol-related callers increased. The number of calls for cannabis and methamphetamine remained stable. Results suggest the helpline was not used to its full capacity, suggesting a role for further promotion during times of crises.

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Evaluation of the Safer Use of Antipsychotics in Youth Study on Population Level Antipsychotic Initiation: An Interrupted Time Series Analysis.

Background: Antipsychotics carry a higher-risk profile than other psychotropic medications and may be prescribed for youth with conditions in which other first-line treatments are more appropriate. This study aimed to evaluate the population-level effect of the Safer Use of Antipsychotics in Youth (SUAY) trial, which aimed to reduce person-days of antipsychotic use among participants. Methods: We conducted an interrupted time series analysis using segmented regression to measure changes in prescribing trends of antipsychotic initiation rates pre-SUAY and post-SUAY trial at four U.S. health systems between 2013 and 2020. Results: In our overall model, adjusted for age and insurance type, antipsychotic initiation rates decreased by 0.73 (95% confidence interval [CI]: 0.30, 1.16, p = 0.002) prescriptions per 10,000 person-months before the SUAY trial. In the first quarter following the start of the trial, there was an immediate decrease in the rate of antipsychotic initiations of 6.57 (95% CI: 0.99, 12.15) prescriptions per 10,000 person-months. When comparing the posttrial period to the pretrial period, there was an increase of 1.09 (95% CI: 0.32, 1.85) prescriptions per 10,000 person-months, but the increasing rate in the posttrial period alone was not statistically significant (0.36 prescriptions per 10,000 person-months, 95% CI: -0.27, 0.99). Conclusion: The declining trend of antipsychotic initiation seen between 2013 and 2018 (pre-SUAY trial) may have naturally reached a level at which prescribing was clinically warranted and appropriate, resulting in a floor effect. The COVID-19 pandemic, which began in the final three quarters of the posttrial period, may also be related to increased antipsychotic medication initiation.

Analysis of time series using the fractal method

Analysis of time series using the fractal method

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Monetary Policy, Digitalisation and Banking Industry Performance in Nigeria

In the evolving financial landscape of Nigeria, the interplay between monetary policy and digitalization significantly influences the performance metrics of banks. This study explores the multifaceted impact of these two critical factors on the Nigerian banking sector. Monetary policy, orchestrated by the Central Bank of Nigeria, regulates the economy's money supply, affecting banks' lending behaviors and liquidity. Digitalization, on the other hand, offers a transformative potential for banks to enhance operational efficiency, customer experience, and service delivery through technological advancements. The study examines how money supply, lending rate and digitalization (proxied by mobile cell subscription) affect banks’ financial performance, focusing on bank credit and liquid assets, while controlling for inflation and exchange rate variables. It also examines the interaction between digitalization and monetary policy variables on bank performance. Employing an interactive multiple regression model, the study analyzes time series data from 1996 to 2022 sourced from the World Bank. Findings indicate that while stringent monetary policies can constrain bank performance by tightening credit and reducing liquidity, digitalization provides an opportunity for banks to counteract these effects through enhanced efficiency. However, the successful integration of digital technologies is contingent upon adequate investment, regulatory support, and strategic alignment with the banks’ core objectives. The study concludes that Nigerian banks must adeptly manage monetary policy fluctuations and leverage digitalization to sustain and enhance performance. These insights could guide policy formulation and strategic decision-making within the banking industry.

Revealing Urban Deformation Patterns through InSAR Time Series Analysis with TCN and Transfer Learning

Abstract. Current multi-epoch InSAR techniques heavily rely on the assumption of linear deformation. This can sometimes overlook crucial deformation signals when using velocities for evaluation. The process of interpreting InSAR time series is not only time-consuming and labor-intensive but also requires a certain level of expertise. This study refines existing InSAR deformation categories, such as stable, linear, step, piecewise linear, power, and undefined, to define 'canonical deformation time series patterns.' We propose an innovative approach for InSAR post-processing using Temporal Convolutional Networks (TCN) and transfer learning. Due to the limited availability of real data, we use simulated data to train a pre-existing model. We then assess the effectiveness of our method in identifying urban deformation patterns. This research could significantly improve our understanding of large-scale InSAR time series deformation and reveal the underlying patterns.

Development of and interplay between grit and peer learning among adolescent English-as-a-foreign-language learners in China: a time series analysis study

Development of and interplay between grit and peer learning among adolescent English-as-a-foreign-language learners in China: a time series analysis study

Random and time-persistent depositional processes in turbidite successions: an example from the marine deep-water Aoshima Formation (Neogene, Kyushu Island, southwest Japan)

ABSTRACT The deposits of flood- and earthquake-derived subaqueous sediment gravity flows represent a significant fraction of lacustrine and deep-sea sedimentary successions, thus providing a valuable record of such natural disasters. The magnitude of these events and the thickness of the associated deposits are considered to follow a lognormal or power-law frequency distribution, whilst that of time intervals between subsequent events appear to be best approximated by a Poisson model, indicative of a random, time-independent phenomenon. However, the debate on whether the sedimentary record of these natural disasters is governed by randomness alone or whether there is some underlying stratigraphic ordering is still unsettled and requires detailed time-series analysis. This study consists of a time-series analysis of mudstone- and sandstone-dominated turbidite successions offshore a fan-delta system in the Neogene Aoshima Formation that belongs to the sedimentary fill of the forearc basin of southwest Japan. The formation consists of a monotonous alternation of very fine- to medium-grained sandstones capped by hemipelagic mudstones and, more rarely, by turbidite mudstones. The results show that the autocorrelation function of the time series suggests quasi-periodic variability in the upper sandstone-dominated part, whereas the lower mudstone-dominated part shows a white-noise-like pattern. Rescaled range analysis shows that the number of events per unit time in the lower part is characterized by a random time series, such as Brownian noise with a Hurst exponent of 0.5. In contrast, the thickness of event beds of the lower part and the thickness and the number of events of the upper part are persistent time series with a Hurst exponent &amp;gt; 0.5. These results suggest that the number of turbidite depositional events in the mudstone-dominated part indicates random timing, whereas its thickness time series and the sandstone-dominant part are not governed by simple stochastic processes but are affected by sea-level changes, sediment transport dynamics, and other factors such as, for example, seafloor topography.

Seasonal Scale Climatic Factors on Grassland Phenology in Arid and Semi-Arid Zones

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

Identification of Key Areas for Ecological Conservation and Restoration based on Multi-source Data and Remote Sensing Time Series Analysis

Abstract. Ecological conservation and restoration of national land space is an important aspect for implementing the national ecological civilization strategy, and an important measure for establishing the national ecological security pattern and coordinating the systematic management of mountains, water, forests, fields, lakes, grass and sand. The identification of key areas for ecological conservation and restoration is a precondition to spatially and temporally laying out ecological conservation and restoration planning, which is crucial for the ecological conservation and restoration of national land space. Therefore, this paper considers Henan Province as the study area and carries the research and practice of identifying key areas for ecological conservation and restoration by integrating multi-source data and remote sensing time series analysis. Firstly, based on the Google Earth Engine (GEE) cloud platform and MOD13A1 data, the Mann-Kendall method is used to analyze the trend of time series NDVI in the study area from 2011 to 2020. Secondly, based on multi-source data such as meteorological, soil, topographic, net primary production (NPP), etc., an ecosystem health analysis and assessment based on ecosystem service trade-off and synergy is used to select ecological source. Finally, based on the results of time series NDVI analysis and ecological source selection, fusion analysis is used to identify key areas for ecological conservation and restoration. The results show that this proposed research framework not only considers the “static” ecosystem services and ecosystem health attributes, but also measures the “dynamic” ecosystem change trends. It can effectively identify key areas for ecological conservation and restoration in national space. The results of the study can provide technical support for the background survey, problem identification, planning and engineering layout of ecological conservation and restoration in the national land space.

Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis

BackgroundThe 2013 ACC/AHA Guideline was a paradigm shift in lipid management and identified the four statin-benefit groups. Many have studied the guideline’s potential impact, but few have investigated its potential long-term impact on MACE. Furthermore, most studies also ignored the confounding effect from the earlier release of generic atorvastatin in Dec 2011.MethodsTo evaluate the potential (long-term) impact of the 2013 ACC/AHA Guideline release in Nov 2013 in the U.S., we investigated the association of the 2013 ACC/AHA Guideline with the trend changes in 5-Year MACE survival and three other statin-related outcomes (statin use, optimal statin use, and statin adherence) while controlling for generic atorvastatin availability using interrupted time series analysis, called the Chow’s test. Specifically, we conducted a retrospective study using U.S. nationwide de-identified claims and electronic health records from Optum Labs Database Warehouse (OLDW) to follow the trends of 5-Year MACE survival and statin-related outcomes among four statin-benefit groups that were identified in the 2013 ACC/AHA Guideline. Then, Chow’s test was used to discern trend changes between generic atorvastatin availability and guideline potential impact.Results197,021 patients were included (ASCVD: 19,060; High-LDL: 33,907; Diabetes: 138,159; High-ASCVD-Risk: 5,895). After the guideline release, the long-term trend (slope) of 5-Year MACE Survival for the Diabetes group improved significantly (P = 0.002). Optimal statin use for the ASCVD group also showed immediate improvement (intercept) and long-term positive changes (slope) after the release (P < 0.001). Statin uses did not have significant trend changes and statin adherence remained unchanged in all statin-benefit groups. Although no other statistically significant trend changes were found, overall positive trend change or no changes were observed after the 2013 ACC/AHA Guideline release.ConclusionsThe 2013 ACA/AHA Guideline release is associated with trend improvements in the long-term MACE Survival for Diabetes group and optimal statin use for ASCVD group. These significant associations might indicate a potential positive long-term impact of the 2013 ACA/AHA Guideline on better health outcomes for primary prevention groups and an immediate potential impact on statin prescribing behaviors in higher-at-risk groups. However, further investigation is required to confirm the causal effect of the 2013 ACA/AHA Guideline.

Filtering Empty Video Frames for Efficient Real-Time Object Detection

Deep learning models have significantly improved object detection, which is essential for visual sensing. However, their increasing complexity results in higher latency and resource consumption, making real-time object detection challenging. In order to address the challenge, we propose a new lightweight filtering method called L-filter to predict empty video frames that include no object of interest (e.g., vehicles) with high accuracy via hybrid time series analysis. L-filter drops those frames deemed empty and conducts object detection for nonempty frames only, significantly enhancing the frame processing rate and scalability of real-time object detection. Our evaluation demonstrates that L-filter improves the frame processing rate by 31–47% for a single traffic video stream compared to three standalone state-of-the-art object detection models without L-filter. Additionally, L-filter significantly enhances scalability; it can process up to six concurrent video streams in one commodity GPU, supporting over 57 fps per stream, by working alongside the fastest object detection model among the three models.

Evaluating the temperature–mortality relationship over 16 years in Cyprus

ABSTRACT In many regions of the world, the relationship between ambient temperature and mortality is well-documented, but little is known about Cyprus, a Mediterranean island country where climate change is progressing faster than the global average. We Examined the association between daily ambient temperature and all-cause mortality risk in Cyprus. We conducted a time-series analysis with quasipoisson distribution and distributed lag non-linear models to investigate the association between temperature and all-cause mortality from 1 January 2004 to 31 December 2019 in five districts in Cyprus. We then performed a meta-analysis to estimate the overall temperature-mortality dose-response relationship in Cyprus. Excess mortality was computed to determine the public health burden caused by extreme temperatures. We did not find evidence of heterogeneity between the five districts (p = 0.47). The pooled results show that for cold effects, comparing the 1st, 2.5th, and 5th percentiles to the optimal temperature (temperature associated with least mortality, 25 ℃), the overall relative risks of mortality were 1.55 (95% CI: 1.32, 1.82), 1.41 (95% CI: 1.21, 1.64), and 1.32 (95% CI: 1.15, 1.52), respectively. For heat effects, the overall relative risks of mortality at the 95th, 97.5th and 99th percentiles were 1.10 (95% CI: 1.04, 1.16), 1.17 (95% CI: 1.07, 1.29), and 1.29 (95% CI: 1.11, 1.5), respectively. The excess mortality attributable to cold days accounted for 8.0 deaths (95% empirical CI: 4.5–10.8) for every 100 deaths, while the excess mortality attributable to heat days accounted for 1.3 deaths (95% empirical CI: 0.7–1.7) for every 100 deaths. The results prompt additional research into environmental risk prevention in this under-studied hot and dry region that could experience disproportionate climate change related exposures. Implications: The quantification of excess mortality attributable to temperature extremes shows an urgent need for targeted public health interventions and climate adaptation strategies in Cyprus and similar regions facing rapid climate change. Future steps should look into subpopulation sensitivity, coping strategies, and adaptive interventions to reduce potential future risks.

Utilizing InSAR for Surface Stability Monitoring in Mining Sites: A Case Study of Sukari Gold Mine in Egypt

Abstract. The monitoring of mining sites is crucial as a result of the safety and environmental issues linked to mining activities. Traditional surveying and monitoring tools exhibit limitations in terms of spatial coverage and are associated with various concerns regarding time and cost efficiency. Hence, the mining industry has shown increased interest in the time-series analysis of interferometric synthetic aperture radar (InSAR). This is primarily due to its precision and wide spatial coverage advantages. This study employed an InSAR technology, persistent scatterer interferometry (PSI), to identify potential geohazards within the Sukari gold mine (SGM), located in Egypt. A total of 23 images were collected from the ascending orbit of Sentinel-1 sensor between January 7, 2022, and November 3, 2022, for the SGM mining location. Results revealed relatively stable measurements in non-mining areas of the site, with a surface displacement within ±10mm per year in the line-of-sight (LOS) direction. However, several active mining operation areas showed significant surface motion. Various sections of the open pit of the SGM exhibited displacement, including the southwestern edge (-16.8mm) and the northeastern edge (35.7mm), and the highest velocity was detected at a section of the western edge, which demonstrated an annual displacement of -64mm in the LOS. Additionally, notable surface motion was observed in some parts of the two tailings storage facilities at the site. The results of this study illustrate the importance of InSAR techniques in mining site-wide coverage stability monitoring.

A Comparative Analysis of the Level of Direct Commercial and Financial Relations: A Case Study of the Two Sides of the Taiwan Straits in the Past Ten Years

This research conducts a comprehensive analysis of the direct commercial and financial relations between Taiwan and mainland China over the past ten years, aiming to understand the depth, trends, and impacts of economic ties. The study employs a comparative analysis approach to leverage data from reputable sources such as the International Monetary Fund (IMF), World Bank, and official reports from Taiwan and China. The analysis of trade patterns reveals a consistent increase in volumes, showcasing a maturing economic relationship marked by the diversification of traded goods. Cross-Strait investments, particularly in critical sectors like technology and finance, highlight strategic collaborations and sector-specific interdependence. Financial relationships, examined through IMF reports and national financial institutions, demonstrate a deepening integration of banking ties despite geopolitical complexities. The study employs theoretical frameworks such as Complex Interdependence Theory and Political Economy Theory to interpret findings. The data analysis includes time-series analysis techniques to identify seasonality, trends, and cyclical patterns, providing insights for policymakers and businesses. The study's significance lies in its contribution to understanding the multifaceted nature of cross-Strait economic relations, emphasizing the endurance of economic interactions despite political complexities. The findings provide a nuanced foundation for improved bilateral relations, offering policymakers insights for strategic adjustments and fostering sustainable economic collaboration. In conclusion, this research contributes to the academic discourse on Taiwan-China economic relations by providing a detailed and empirically supported analysis. It addresses the intricacies of trade, investments, and financial collaborations, shedding light on the dynamics shaping the economic ties between these two entities.

Machine Learning for Economic Forecast Based on Past 30 Years and Covid-19 Pandemic Data

Along the time, there are many forecasting techniques applied by researchers in various topics in Economics. Traditionally, some of the most popular time-series analysis model like Vector-autoregressive (VAR) are still widely applied in Economics forecasting topics since they can capture sufficient information for a dataset to predict. On the other hands, machining learning and deep-learning algorithm also shades a way for forecasting. In particular, algorithm like Gradient boosting, learning algorithm Long-short-term memory (LSTM) shows their tremendous accuracy when applying to Economic data. Covid-19 leads to one of the largest economic recessions in history in terms of intensity and time. In this article, some of the popular forecasting methods are applied to forecasting GDP data with the record from Covid-19. The empirical analysis is conducted for data in United states to compare the accuracy and divergency for various prediction algorithms. It turns out that machine learning algorithms are well performed in US cases where they generate relatively low forecasting error.

A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.