scholarly journals Predicting drought using Bayesian structural time series model

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Sophia Wang ◽  
Connor Lee ◽  
XL Pang
Keyword(s):  
Time Series ◽  
Well Being ◽  
The United States ◽  
Fire Season ◽  
Series Data ◽  
Percentage Error ◽  
Structural Time Series ◽  
Structural Time ◽  
Model Training ◽  
The Impact

The western U.S. has been experiencing a mega-scale drought since 2000. By killing trees and drying out forests, the drought triggers widespread wildfire activities. In the 2020 California fire season alone, more than 10.3 million acres of land were burned and over 10000 structures were damaged. The estimated cost is over $12 billion. Drought also devastates agriculture and drains the social and emotional well-being of impacted communities.  This work aims at predicting the occurrence and severity of drought, and thus helping mitigate drought related adversaries. A machine learning based framework was developed, including time series data collection, model training, forecast and visualization. The data source is from the National Drought Monitor center with FIPS (Federal Information Processing Standards) geographic identification codes. For model training and forecasting, a Bayesian structural time series (BSTS) based statistical model was employed for a time-series forecasting of drought spatially and temporally. In the model, a time-series component captures the general trend and seasonal patterns in the data; a regression component captures the impact of the drought in measurements such as severity of drought, temperature, etc. The statistical measure, Mean Absolute Percentage Error, was used as the model accuracy metric. The last 10 years of drought data up to 2020-09-01 was used for model training and validation. Back-testing was implemented to validate the model . Afterwards, the drought forecast was generated for the upcoming 3 weeks of the United States based on the unit of county level. 2-D heat maps were also integrated for visual reference.   

scholarly journals The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model

2021 ◽  
Vol 10 (2) ◽  
pp. 1-17
Author(s):  
Ondrej Bednar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Central Bank ◽  
Causal Effect ◽  
Time Series Model ◽  
Structural Time Series ◽  
Causal Impact ◽  
Structural Time ◽  
The Impact

I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.

scholarly journals Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management

2019 ◽  
Vol 11 (18) ◽  
pp. 4945 ◽  
Author(s):  
Sunghae Jun
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Bayesian Regression ◽  
Series Data ◽  
Sustainable Technology ◽  
Structural Time Series ◽  
Sustainable Technologies ◽  
Technology Analysis ◽  
Management Of Technology ◽  
Structural Time

Many companies take the sustainability of their technologies very seriously, because companies with sustainable technologies are better able to survive in the market. Thus, sustainable technology analysis is important issue in management of technology (MOT). In this paper, we study the management of sustainable technology (MOST). This focuses on the sustainable technology in various MOT fields. In the MOST, sustainable technology analysis is dependent on time periods. We propose a method of sustainable technology analysis using a Bayesian structural time series (BSTS) model based on time series data. In addition, we use the Bayesian regression to find the relational structure between technologies. To show the performance of our method and how the method can be applied to practical works, we carry out a case study using the patent data related to artificial intelligence technologies.

scholarly journals Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

2021 ◽  
Author(s):  
Meshrif Alruily ◽  
Mohamed Ezz ◽  
Ayman Mohamed Mostafa ◽  
Nacim Yanes ◽  
Mostafa Abbas ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Computational Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Emerging Infectious Diseases ◽  
The United States ◽  
Series Data ◽  
Percentage Error ◽  
Google Search

ABSTRACTAccurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.

scholarly journals Investigating the Dynamic Empirics between Outflow of Remittances and Economic Growth: A Case Study of Saudi Arabia

2018 ◽  
Vol III (I) ◽  
pp. 98-104
Author(s):  
Taseer Salahuddin ◽  
Muhammad Awais ◽  
Asif Raza
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Saudi Arabia ◽  
Empirical Investigation ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Skilled Workers ◽  
The Impact ◽  
Adf Test

To meet the developmental goals and fulfill the domestic labor demand of Saudi Arabia, more than 9.5 million skilled, semi-skilled and highly skilled workers from across the world are imported. After the United States of America (USA), it is the second-largest which is responsible for the larger outflow of remittances. This study is an empirical investigation to explore the impact of remittances outflow on the economic growth activity of Saudi Arabia. Furthermore, it also tests whether this outflow causes inflation in the Saudi economy. For the sack of empirical investigation, this study utilized annual time series data ranging from 1970 to 2017. Before estimating the shortrun and long-run estimates by the application of the AutoRegressive Distributed Lag (ARDL) method, time-series properties of data are explored using the Augmented Dicky Fuller (ADF) test.

scholarly journals TD 2590 - Aplicação de Modelos Dinâmicos Bayesianos para Projeção de Arrecadação Tributária

2020 ◽  
Keyword(s):  
Time Series ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Structural Time Series ◽  
Absolute Percentage Error ◽  
Structural Time

Neste estudo, empreende-se uma análise econométrica, com vistas à projeção das séries desagregadas do Imposto sobre Operações Relativas à Circulação de Mercadorias e Prestação de Serviços de Transporte Interestadual e Intermunicipal e de Comunicação (ICMS), administradas pelo Conselho Nacional de Política Fazendária do Ministério da Economia (Confaz/ME). Três metodologias foram aplicadas: i) o modelo estrutural dinâmico (MED) – por meio da bayesian structural time series (BSTS); ii) o modelo linear dinâmico (MLD); e iii) o modelo fatorial dinâmico (MFD), todos estes estimados com base na prática bayesiana. Os exercícios econométricos objetivaram três tipos de resultados: i) a avaliação da projeção; ii) a elasticidade do tributo em relação ao fato gerador; e iii) a projeção sessenta meses à frente fora da amostra. Nossa base de dados é composta de dados no período entre janeiro de 2006 a dezembro de 2019. Tendo-se em vista a dificuldade para tratar as séries do Confaz devido à falta de regularidade, os exercícios feitos para validação da projeção apresentaram performance bastante razoável. De cerca de vinte séries para cada estado, aproximadamente 80% registram um erro percentual médio absoluto (Mape – em inglês, mean absolute percentage error) abaixo de 15%.

scholarly journals Modeling U.S. Broiler Supply Response: A Structural Time Series Approach

1998 ◽  
Vol 27 (2) ◽  
pp. 241-251 ◽  
Author(s):  
Crispin M. Kapombe ◽  
Dale Colyer
Keyword(s):  
Time Series ◽  
The United States ◽  
Time Series Model ◽  
Poultry Production ◽  
Seasonal Component ◽  
Supply Response ◽  
Structural Time Series ◽  
Time Series Approach ◽  
Structural Time ◽  
Feed Costs

A structural time series model is used to estimate the supply response function for broiler production in the United States using quarterly data and a structural time series model. This model has the advantage of expressing trend and seasonal elements as stochastic components, allowing a dynamic interpretation of the results and improving the forecast capabilities of the model. The results of the estimation indicate the continued importance of feed costs to poultry production and of technology as expressed by the stochastic trend variable. However, seasonal influences appear to have become less important, since the seasonal component was not statistically significant.

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