ARIMA Research Articles

Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI

Abstract: The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. With the ongoing global energy transition and the push for low-carbon development, predicting electricity prices has become increasingly important for policymakers and market participants. This paper explores the forecasting capabilities of the ARIMA and LSTM models in analyzing electricity prices in the United States, drawing from data spanning 2001 to 2024.ARIMA, a traditional time series model, is valued for its simplicity and effectiveness in capturing linear trends, while LSTM, a deep learning-based model, excels at handling long-term dependencies in complex datasets. This study reveals that while both models offer valuable insights, each exhibits limitations. ARIMA struggles with non-linear patterns and volatility, whereas LSTM tends to underestimate extreme price values. The findings highlight the potential of hybrid models that combine traditional and machine learning approaches to enhance forecasting accuracy in the increasingly dynamic energy market. This research provides essential guidance for improving decision-making processes in the context of the global shift towards clean energy.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Impact of Low Physical Activity on Cardiovascular Disease Across Regions and Demographic Groups: Insights from the Global Burden of Disease Study.

Cardiovascular disease (CVD) is a leading cause of death globally, with low physical activity (LPA) as a significant modifiable risk factor. The prevalence of LPA remains high, necessitating a comprehensive assessment of its impact on CVD. We applied Joinpoint regression to assess trends in deaths and Disability-Adjusted Life Years (DALYs) and employed ARIMA models to project future BMI-related burdens. From 1990 to 2021, CVD-related deaths due to LPA rose from 218,938 to 371,736 globally, with the most significant increases in Southeast Asia and Sub-Saharan Africa. DALYs surged from 4.47 million to 7.29 million. Although age-standardized death rates showed a slight decline in high-income countries (-2.27% EAPC), lower-income regions experienced a steady rise. YLDs grew from 344,680 to 725,181, while YLLs increased from 4.13 million to 6.57 million, with older adults (75+ years) carrying the highest burden. The growing burden of CVD linked to LPA highlights the urgent need for interventions, particularly in low- and middle-income countries, to reduce future risks and improve public health outcomes.

Characteristics of Machine Learning-based Univariate Time Series Imputation Method

Handling missing values in univariate time series analysis poses a challenge, potentially leading to inaccurate conclusions, especially with frequently occurring consecutive missing values. Machine Learning-based Univariate Time Series Imputation (MLBUI) methods, utilizing Random Forest Regression (RFR) and Support Vector Regression (SVR), aim to address this challenge. Considering factors such as time series patterns, missing data patterns, and volume, this study explores the performance of MLBUI in simulated Autoregressive Integrated Moving Average (ARIMA) datasets. Various missing data scenarios (6%, 10%, and 14%) and model scenarios (Autoregressive (AR) models: AR(1) and AR(2); Moving Average (MA) models: MA(1) and MA(2); Autoregressive Moving Average (ARMA) models: ARMA(1,1) and ARMA(2,2); and Autoregressive Integrated Moving Average (ARIMA) models: ARIMA(1,1,1) and ARIMA(1,2,1)) with different standard deviations (0.5, 1, and 2) were examined. Five comparative methods were also used in this research, including Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The research findings indicate that MLBUI performs exceptionally well in imputing successive missing values. The results of this study indicate that the performance of MLBUI in imputing consecutive missing values, based on MAPE, yielded values of less than 10% across all scenarios used.

Comparative Study on Forecast Performance from Decomposition, Winter’s and Sarima Models

This study investigates the forecasting accuracy of three univariate time series models—Decomposition, Winter’s method, and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict the Agricultural GDP of Nigeria. Quarterly data on Nigeria's Agricultural GDP from 2010 to the first quarter of 2023, obtained from the National Bureau of Statistics, were analyzed. The study applied Box-Jenkins SARIMA modeling, time series decomposition, and Winter’s method to compare their forecasting accuracy using Root Mean Square Error (RMSE) as the selection criterion. The results revealed that the SARIMA (0, 0, 2)(2, 1, 0) model outperformed the other methods, with the lowest RMSE, indicating its superior accuracy in forecasting Agricultural GDP. Winter’s method and the Decomposition method. The forecast from the SARIMA model indicated a positive trend in Nigeria’s Agricultural GDP over the study period, reinforcing the sector’s critical role in economic growth.

Stock Price Prediction Based on BP Neural Network and ARIMA Model

Stock Price Prediction Based on BP Neural Network and ARIMA Model

Analysis of Stroke Burden in China from 1990 to 2021 and Projections for the Next Decade.

This study aims to describe the temporal trends of stroke burden in different age and sex groups in China from 1990 to 2021, including incidence, prevalence, mortality, and disability-adjusted life years (DALYs), and to compare these data with global figures. Using data from the Global Burden of Disease (GBD) database from 1990 to 2021, we analyzed changes in stroke burden in China and globally. The average annual percentage change (AAPC) was calculated using Joinpoint software to reflect trends. Stroke burden differences across various age groups and sexes were compared, and the ARIMA model was utilized for future projections. Between 1990 and 2021, the age-standardized incidence rate (ASIR) of stroke in China decreased from 226.938 to 204.753 per 100,000, while the global ASIR decreased from 180.973 to 141.553 per 100,000. The age-standardized prevalence rate (ASPR) in China increased from 1167.425 to 1301.42 per 100,000, whereas the global ASPR decreased from 1201.111 to 1099.31 per 100,000. The age-standardized mortality rate (ASMR) in China fell from 242.18 to 138.029 per 100,000, while the global ASMR decreased from 144.313 to 87.454 per 100,000. China's age-standardized DALY rate (ASDR) declined from 4834.791 to 2648.025 per 100,000, whereas the global ASDR decreased from 3078.952 to 1886.196 per 100,000. The AAPCs for ASIR, ASPR, ASMR, and ASDR in China were -0.370%, 0.326%, -1.793%, and -1.933%, respectively, compared to -0.291%, -0.807%, -1.601%, and -1.570% globally from 1990 to 2021. Age and sex significantly influenced the stroke burden, with higher incidence and mortality rates in males than in females. Projections for the next decade indicate that stroke incidence in China will remain stable, with an expected rise in ASPR, and declines in ASMR and ASDR. Globally, ASIR is expected to decline, while ASPR will rise, and ASMR and ASDR will continue to decrease. From 1990 to 2021, the incidence, mortality, and DALYs of stroke in China have decreased, while the prevalence has increased. Stroke burden is age-related, with higher prevalence in older adults and higher mortality in the elderly. Males are more susceptible to stroke and have a higher risk of death. Over the next decade, stroke prevalence in China is expected to rise, posing challenges due to population aging, even as mortality and disability rates decline. Sustained public health efforts will be necessary.

News Popularity Prediction in West Sumatera Using Autoregressive Integrated Moving Average

The increasing public interest in reading online news is undoubtedly a challenge for news portals as online news providers. Therefore, this research was conducted to predict news popularity in West Sumatra through the FajarSumbar.com news portal using the Autoregressive Integrated Moving Average (ARIMA) model. This research aims to develop a forecasting model that can assist in estimating the popularity of each news category so that news portals can devise more effective content strategies. The data used in this study includes the number of monthly news impressions from March 2021 to June 2024, which are grouped into various categories such as Religion Culture, Industrial Economics, Criminal Law, etc. Using the ARIMA method, which can handle time series data and overcome data non-stationarity problems through differencing and the use of grid search in optimization to find the best parameters based on the lowest evaluation metric. The results show that the ARIMA model can provide reasonably accurate predictions, although the level of accuracy varies between categories. The Mean Absolute Percentage Error (MAPE) values obtained are as follows: Religion Culture 26%, Industrial Economy 29%, Criminal Law 29%, Health 40%, Sports 38%, Tourism Entertainment 26%, Education 27%, Government Politics 31%, Social Environment 27%, and Technology 51%. The Technology and Health news categories show higher error rates than others, while Religion Culture and Tourism Entertainment have better accuracy rates. Thus, the ARIMA model can be used to predict future trends in news popularity, helping editors plan content strategies that are more relevant and interesting to readers. However, improvements are needed for news categories that have high variability.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Spatiotemporal distribution and meteorological factors of hemorrhagic fever with renal syndrome in Hubei province.

Hemorrhagic fever with renal syndrome (HFRS) is a vital rodent-borne disease, and poses a serious public health threat in Hubei province. We aimed to explore the spatiotemporal distribution of HFRS in Hubei province during 2005-2022, and the effects of meteorological factors. Data on HFRS cases at the county level in Hubei province during 2005-2022 were obtained from the Chinese Center for Disease Prevention and Control Information System. The monthly meteorological data at the city level was extracted from the China Meteorological Data Sharing Service System from 2016 to 2020. Descriptive analyses, joinpoint regression model, spatial correlation analyses, Geodetector model and autoregressive integrated moving average (ARIMA) model were conducted to investigate the epidemic characteristics, temporal trend, spatial distribution, influencing factors of HFRS and predict its trend. A total of 6,295 cases were reported in Hubei province during 2005-2022, with an average incidence of 6/1,000,000. Most cases were males (74.52%) and aged 40-69 years (71.87%). The monthly HFRS cases showed two seasonal peaks, which were summer (May to June) and winter (November to December). The HFRS incidence remained fluctuating at a low level during 2005-2015, followed an increasing trend during 2015-2018, and then decreased during 2018-2022. Hotspots were concentrated in the center of Hubei province in all 3 periods, including Qianjiang, Tianmen and some counties from Xiangyang, Jingmen and Jingzhou cities. The distribution of HFRS had a positive association with wind speed, while a "V"-shaped correlation with mean temperature, with an explanatory power of 3.21% and 1.03% respectively (both P <0.05). The ARIMA model predicted about 1,223 cases occurred in the next 3 years. HFRS cases showed seasonal fluctuation and spatial clustering in Hubei province. Central plain areas showed high risk of HFRS. Wind speed and mean temperature had significant effects on the transmission of HFRS in Hubei province. The results alert health authorities to conduct disease-climate surveillance and comprehensive prevention strategies, especially in high-risk counties.

Optimization of Marginal Price Forecasting in Mexico through applying Machine Learning Models

The Local Marginal Price (LMP) represents the value of energy at a specific moment and location, and its proper management is crucial for the development of the country's strategic sectors. This study compares the ADR, RPSG, SARIMA, and LSTM-H models for predicting the LMP, achieving an approximate effectiveness of 88%. By implementing it in 28 nodes of the three interconnection systems (SIN, BCA, and BCS) in Mexico, the results of the enhanced LSTM network analysis are presented through sensitivity analysis and an ensemble with Prophet, yielding the following metrics: MAE: 0.0189, MSE: 0.0101, RMSE: 0.1007, and MAPE: 12.18, at node 05PAR-115 in Hidalgo del Parral, Chihuahua. This model can construct tree diagrams (ADR) that identify the critical variables for predicting the LMP of any node, significantly contributing to the accuracy of predictive analysis models.

MODELING TOURIST ARRIVALS IN THE ISLAND GARDEN CITY OF SAMAL: IMPLICATIONS TO BUSINESS OPERATIONS OF TOURISM-RELATED INDUSTRY

&lt;p&gt;This study aimed to model the tourist arrivals in the Island Garden City of Samal (IGaCoS) by utilizing the autoregressive integrated moving average (ARIMA) method. Specifically, this paper sought to determine whether there was a statistically appropriate forecasting model using the ARIMA approach, based on which it would be possible to reasonably forecast the number of tourist arrivals in IGaCoS. Quantitative secondary data were analyzed by employing the Box-Jenkins ARIMA. The time series plot was characterized by an apparent increasing trend combined with intermittent sinusoidal oscillations. The time series revealed a steady, gradual increase up until 2020. Next, a sharp decline was observed midway through 2020, indicative of the ill effects of the COVID-19 pandemic. A gradual increase was noted once more, starting in 2021, with some fluctuations. The Augmented Dickey-Fuller (ADF) test revealed that the time series was non-stationary at level— but was made stationary after differenced twice. The ARIMA (1,2,1) model was found to be statistically significant. The fitted model initially showed some notable deviations from the original data, but, with time, converged closely to the actual values, proving it is a reliable way to represent underlying patterns. The ARIMA (1,2,1) model, selected for having the lowest AIC and BIC values and a MAPE within the acceptable range for reasonable forecasting, was used to predict tourist arrivals in IGaCoS over the next six months. The forecasted values suggested a mix of fluctuations and stability. There was a noticeable upward trajectory at the beginning, which was followed by a slight decline at the end that yet remained stable. The forecasted results from the ARIMA (1,2,1) model may provide businesses with a roadmap for comprehensive planning in terms of operational and strategic concerns. This study helped address Sustainable Development Goal (SDG) No. 8, Decent Work and Economic Growth.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;JEL:&lt;/strong&gt; L83, R11, C53&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0776/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

Machine learning for predicting tourist spots’ preference and analysing future tourism trends in Bangladesh

ABSTRACT This study uses machine learning, including Support Vector Machines, Decision Trees, K-Nearest Neighbors, to examine Bangladesh’s tourism industry to forecast traveller preferences. We use time series analysis, including ARIMA, Moving Average, and Auto-regression models, to predict future tourism trends. Our results show that, with an accuracy of 96.3%, Linear SVM was the best at predicting preferences. For trend forecasting, the ARIMA model fared better than the others, suggesting that Bangladeshi tourism may be headed in an undesirable direction. Our observations and insights can help guide strategic choices and decisions in the creation of policies and the administration of tourism.

Arowana cultivation water quality monitoring and prediction using autoregressive integrated moving average

&lt;p class="western" align="justify"&gt;&lt;span&gt;Decorative fish is a fish that humans keep for amusement. There are many decorative fish that exist in this world, one of them is known as the Arowana fish (Scleropages Formosus). This fish is known around Asia including in Indonesia. However, to ensure the Arowana is living well is not easy. The water quality inside a farm must follow a strict balance. The pH of the water must not exceed or below 7 pH. Meanwhile, the total dissolved solid (TDS) salt must not exceed 1000 parts per million. If the balance collapsed, the Arowana fish will not grow. Thus, the owner must monitor the water to make sure that the water is ideal. There were many approaches including internet of things (IoT) solutions. However, they have weaknesses with prediction. Because of this reason, this study designed pH and TDS monitoring with autoregressive integrated moving average (ARIMA) as the algorithm. To achieve the solution, this study used experiment methodology as the research fundamental from top to bottom. According to the evaluation, this study found that the accuracy of ARIMA model is 98.12% for pH and 98.86% for TDS. On the contrary, the seasonal autoregressive integrated moving average (SARIMA) model has an accuracy of 98.52% for pH and 99.89% for TDS.&lt;/span&gt;&lt;/p&gt;

Decoding Pakistan's rainfall: optimizing predictions from ARIMA to SARIMA with seasonal adjustments

Abstract. This study aims to improve the accuracy of rainfall forecasting in Pakistan by comparatively analyzing the performance of two models, ARIMA and SARIMA, to optimize the forecasting methodology. The study points out that although the ARIMA model performs well in time series analysis, it has shortcomings in handling data with significant seasonal variations. Therefore, the SARIMA model was introduced and it performed better in forecasting seasonal variations. Future research should consider combining the SARIMA model with models that can explain global climate phenomena such as El Nio and La Nia to enhance the accuracy of forecasts. In addition, ways to automate and improve the selection of model parameters should be explored to make the SARIMA model more efficient and accurate. The introduction of the SARIMA model has significantly improved prediction accuracy and contributed to more efficient planning and management of water resources. Areas where improvements can be made include reserving water resources in advance during the dry season or allocating water resources appropriately during the rainy season to support irrigation agriculture, urban water supply, flood control measures, etc. These enhanced forecasting methods help Pakistan cope with climate change challenges.

Forecasting urban unemployment rate in China using ARIMA model

Abstract. The urban unemployment rate is a significant economic indicator that has long drawn researchers interest. Monitoring and predicting changes in the unemployment rate can help in understanding economic trends and implementing appropriate measures. This article aims to forecast urban unemployment rates in China. By collecting previous surveyed urban unemployment rates in China, this article will generate and compare various ARIMA models in order to identify the one with the best forecasting accuracy. The forecast results of the selected model state that the unemployment rate will remain almost unchanged, around 5%, in the second half of 2024 and throughout 2025. Fluctuations are expected to be between 0.01% and 0.03%. The number is much lower than the peak during the pandemic, but it is still above the historical average. This article argues that Chinas economy is gradually stabilizing, and the post-pandemic measures have been effective but are still insufficient. The government still needs to implement additional actions.

Forecast of consumption of natural gas in U.S. based on time series analysis and ARIMA model

Natural gas is a widely used fossil fuel, playing a vital role in both industrial and economic sectors. As a widely used fossil fuel, the prediction of natural gas consumption is crucial for industrial and economics and hence many scholars have been researched on the topic. Due to the weather variation and the sensitivity of industrial demand, a precise consumption forecast is difficult. In the article, ARIMA model, a widely used model in time series analysis and forecasts would be considered in the prediction for consumption for future one year. It turns out that, in the next year, the consumption of natural gas in the coming year is stable and will stay at a still level and the overall trend of the series is increasing, with no significant short-term fluctuations. Therefore, the article suggested that long-term investment in industries related to natural gas is recommended. Besides, short-term investment is not suggested compared to the long-term due to the still level of the consumption within one year.

Utilising ARIMA models to predict the mass balance of the Antarctic ice sheet and comprehend its fluctuations

Abstract. The mass balance of the Antarctic Ice Sheet is forecasted in this study using ARIMA models, with a particular emphasis on the distinct regions of East and West Antarctica. The research endeavours to forecast the trends in ice mass balance over the next 12 years by examining data from 1992 to 2021, thereby elucidating the potential impacts of climate change on these critical regions. The ARIMA model, which is renowned for its capacity to capture and predict time series data, has identified substantial trends that indicate a persistent loss of ice mass, particularly in West Antarctica, which has experienced substantial declines in recent decades. The model's predictions suggest that, despite the potential for a reduction in the rate of loss, the overall trend is consistent with the ongoing reduction of ice mass. These results underscore the pressing necessity for ongoing research and monitoring to gain a more comprehensive understanding of the consequences of these developments. The study also emphasises the necessity of enhancing predictive models by incorporating supplementary environmental variables to provide more comprehensive insights into the future of the Antarctic Ice Sheet and its global impacts, thereby increasing their accuracy.

Multi-Scenario land cover changes and carbon emissions prediction for peak carbon emissions in the Yellow River Basin, China

Research on future land cover changes and carbon emissions is essential for effective land resource management and developing feasible carbon mitigation strategies. This study focused on the Yellow River Basin and employed the Future Land Use Simulation (FLUS) and Autoregressive Integrated Moving Average (ARIMA) models to project future land cover and carbon emissions. Additionally, bivariate spatial autocorrelation was utilized to analyze the relationship between them. Key findings are as follows: 1) Historically, the Yellow River Basin has experienced an expansion in construction land, forests, grasslands, and water, while cropland and unused land have diminished. Notably, construction land displayed the most significant changes, whereas grasslands showed minimal modification. Looking ahead, both the ecological protection and inertial development scenarios exhibit consistent trends with historical patterns across the land type categories. In contrast, the economic priority development scenario forecasts an increase in construction land, cropland, and grasslands, indicating a distinct shift compared to the other scenarios. However, the ecological protection scenario proves to be more sustainable. 2) In the absence of intervention, the simulated carbon emissions from construction land throughout the basin display a linear increase across various scenarios, with provincial-level variations showing an increase from southwest to northeast. However, Henan and Sichuan are expected to experience slower reductions in carbon emissions, compared to other projections. There is a notable positive correlation between carbon emissions and the comprehensive index, indicating that regions with high emissions typically experience substantial land and economic development. 3) Energy consumption projections for 2030 and 2060 indicate that to align with China’s carbon goals, it is essential to reduce energy consumption and adjust the fossil to non-fossil fuel ratio to reduce carbon emissions. Substituting coal with clean energy and enhancing energy efficiency will be more effective for achieving low-carbon emission targets. In summary, this study provides significant guidance for China’s ecological conservation, low-carbon emission strategies, and global carbon emission control efforts.

The impact of the COVID-19 pandemic on the dispensing of systemic anti-cancer therapy (SACT) in Ireland: a population based study

ObjectivesThe COVID-19 pandemic had considerable implications for cancer related care. This study aimed to examine its impact on the dispensing of systemic anti-cancer therapy (SACT) in Ireland. Study designA repeated cross-sectional design was used which involved a quasi-experimental interrupted time series analysis (ITSA), and autoregressive integrated moving average (ARIMA) models. MethodsThis nationally representative study utilised monthly pharmacy claims (i.e. dispensing) data from community and hospital schemes. Dispensed items among individuals prescribed any SACT from January 2019 to April 2021 were included. ResultsDuring the study period, 641,273 SACT items were dispensed, including 57,199 chemotherapeutic agents (8.9%), 15,970 immunotherapeutic agents (2.5%), 87,813 targeted therapies (13.7%), and 480,291 (74.9%) endocrine therapies. There were on average 3.3 and 4.1 fewer immunotherapy and targeted therapy agents, respectively, dispensed per 100,000 population per month post-March 2020 (vs. expected), compared to the level prior to March 2020. For endocrine therapy, there was a significant slowing of the trend post-March 2020 compared to the pre-pandemic period (slope change = -1.72, 95% CI -2.9 to -0.5; p<0.01). ConclusionThere was a significant level decrease in the dispensing of immunotherapy and targeted therapy during the first year of the pandemic, and a slowing of the trend for endocrine therapies. However, no differences in the dispensing of other SACT were observed.