Relevant bibliographies by topics / SARIMA

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'SARIMA'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "SARIMA"

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Permata, Regita Putri, and Ananda Taqhsya Dwiyana. "Comparison of NAIVE, SARIMA, and SARIMAX Models in Short-Term and Long-Term Forecasting of Google Search Trends." International Journal of Scientific Research in Computer Science and Engineering 13, no. 2 (2025): 21–29. https://doi.org/10.26438/ijsrcse.v13i2.665.

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This study examines forecasting methods—Naïve, SARIMA, and SARIMAX—to enhance the accuracy of Google search trend predictions in the fitness industry, specifically for the keyword "Gym." The Naïve method serves as a baseline comparison, SARIMA incorporates seasonal, autoregressive, and moving average components for improved trend detection, while SARIMAX extends SARIMA by integrating exogenous variables. Historical Google search data from 2005 to 2015 is used for model evaluation, with performance assessed using MAE, RMSE, and MAPE metrics. The findings indicate that SARIMAX, which accounts fo
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Ibrahim, A., and A. O. Musa. "ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA." FUDMA JOURNAL OF SCIENCES 7, no. 6 (2023): 24–31. http://dx.doi.org/10.33003/fjs-2023-0706-2095.

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Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines t
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Permatasari, Novia. "Penggunaan Indeks Google Trend Dalam Peramalan Jumlah Pengunjung Taman Rekreasi Selecta Tahun 2020." Seminar Nasional Official Statistics 2021, no. 1 (2021): 1019–24. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.993.

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Kota Batu merupakan salah satu daerah potensi pariwisata di Indonesia, dengan salah satu tujuan pariwisata andalan adalah Taman Rekreasi Selecta. Sejak tahun 2016 hingga 2019, Taman Rekreasi Selecta secara konsisten menjadi tempat wisata dengan jumlah pengunjung terbanyak di Kota Batu. Publikasi data kunjungan wisatawan yang hanya dilakukan sekali dalam satu tahun menunjukkan adanya selang waktu antara pengumpulan dan publikasi data, sehingga pemanfaatan data kunjungan wisatawan tersebut kurang maksimal. Permasalahan tersebut dapat diatasi dengan memanfaatkan real-tima data, yaitu big data. Pa
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Costa, Gabriela Emiliana de Melo e., Frederico Carlos M. de Menezes Filho, Fausto A. Canales, Maria Clara Fava, Abderraman R. Amorim Brandão, and Rafael Pedrollo de Paes. "Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil." Hydrology 10, no. 11 (2023): 208. http://dx.doi.org/10.3390/hydrology10110208.

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Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SA
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Faris Nasirudin and Abdullah Ahmad dzikrullah. "Pemodelan Harga Cabai Indonesia dengan Metode Seasonal ARIMAX." Jurnal Statistika dan Aplikasinya 7, no. 1 (2023): 105–15. http://dx.doi.org/10.21009/jsa.07110.

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Chili is one of the plants favored by the people of Indonesia because Indonesian cuisine is famous for its spicy taste and spices in every food dish. The rise and fall of chili prices in the market are caused by chili farmers whose production decision-making processes are allegedly not handled and supported by a good production and price forecast. Therefore, analysis is needed to see the forecasting of chili prices in Indonesia in the future. The method that researchers use in forecasting in this study is the SARIMA and SARIMAX methods using the variables of rainfall, inflation, and google tre
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Kim, Taereem, Ju-Young Shin, Hanbeen Kim, Sunghun Kim, and Jun-Haeng Heo. "The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models." Water 11, no. 2 (2019): 374. http://dx.doi.org/10.3390/w11020374.

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Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integr
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Woro Tri Handayani, Nandia Rani, Martinus Maslim, and Paulus Mudjihartono. "Forecasting of Catfish Sales by Time Series Using the SARIMA method." Jurnal Buana Informatika 11, no. 2 (2020): 83. http://dx.doi.org/10.24002/jbi.v11i2.3535.

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Abstrak. Sistem informasi yang mengotomatiskan proses bisnis, terutama dengan persyaratan khusus masih relevan saat ini. Clarias Makmur, sebuah usaha mikro di Indonesia yang membiakkan dan menjual ikan lele menggunakan sistem informasi ini untuk menjalankan penjualan, pengeluaran, modal, dan pelaporan mereka. Penjualan ikan lele sebagai makhluk hidup memiliki ciri khas tersendiri yang menunjukkan pola musiman yang unik. Sebuah model bernama SARIMA (Seasonal Autoregressive Integrated Moving Average) kemudian diusulkan untuk memprediksi penjualan. Lebih lanjut, sistem yang disebut SITRAN dibuat
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Chutiman, Nipaporn, Pannarat Guayjarernpanishk, Monchaya Chiangpradit, Piyapatr Busababodhin, Saowanee Rattanawan, and Butsakorn Kong-Led. "The Forecasting Model with Climate Variables of the Re-emerging Disease Rate in Elderly Patients." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (August 4, 2021): 866–75. http://dx.doi.org/10.37394/232015.2021.17.81.

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This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely simil
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Rochayati, Isti, Utami Dyah Syafitri, I. Made Sumertajaya, and Indonesian Journal of Statistics and Its Applications IJSA. "KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT." Indonesian Journal of Statistics and Its Applications 3, no. 1 (2019): 18–32. http://dx.doi.org/10.29244/ijsa.v3i1.171.

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Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The
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Amelia, Ririn, Elyas Kustiawan, Ineu Sulistiana, and Desy Yuliana Dalimunthe. "FORECASTING RAINFALL IN PANGKALPINANG CITY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS (SARIMAX)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (2022): 137–46. http://dx.doi.org/10.30598/barekengvol16iss1pp137-146.

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Changes in extreme rainfall can cause disasters or losses for the wider community, so information about future rainfall is also needed. Rainfall is included in the category of time series data. One of the time series methods that can be used is Autoregressive Integrated Moving Average (ARIMA) or Seasonal ARIMA (SARIMA). However, this model only involves one variable without involving its dependence on other variables. One of the factors that can affect rainfall is wind speed which can affect the formation of convective clouds. In this study, the ARIMA model was expanded by adding eXogen variab
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Dissertations / Theses on the topic "SARIMA"

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Silva, Mário Pedro Cristelo Diogo da. "Previsões das dormidas em hotéis de três regiões de Portugal." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/22816.

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Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e Empresarial<br>This Master's work has as its variable of study overnight stays in hotels in three Portuguese regions - Algarve, Metropolitan Area of Lisbon (AML) and Madeira - seeking to develop forecasting models and to make forecasts for the years 2019 and 2020, subsequently comparing them with the observed values. The variable of interest was chosen because it imposes itself as a faithful portrait of the total tourist demand in Portugal and the regions because they assume themselves as excellent tourist destinations, repre
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Nilsson, Fredrik. "Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors : Payment Volume Forecasting using Hierarchical Regression with SARIMA Errors." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425886.

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When forecasting financial transaction volumes in different markets, different markets often exhibit similar seasonality patterns and public holiday behavior. In this thesis, an attempt is made at utilizing these similarities to improve forecasting accuracy as compared to forecasting each market individually. Bayesian hierarchical regression models with time series errors are used on daily transaction data. When fitting three years of historic data for all markets, no consistent significant improvements in forecasting accuracy was found over a non-hierarchical regression model. When the amount
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Dexheimer, Verena. "Einreisetourismus in Deutschland Paneldatenanalysen und SARIMA-Prognosen." Frankfurt, M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2009. http://d-nb.info/1000128547/04.

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Li, Yangyang M. Eng Massachusetts Institute of Technology. "New product forecasting of appliance and consumables : SARIMA model." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/132738.

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Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, September, 2018<br>Cataloged from the PDF version of thesis.<br>Includes bibliographical references (pages 43-44).<br>Drinkworks is a joint venture between Anheuser-Busch InBev and Keurig Green Mountain, with a focus on developing an in-home alcohol system that can prepare different alcoholic beverages. The goal of this project is to forecast the demand for their new product, consisting of appliance and pods, without historical data. For appliance forecast, this pa
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Trcka, Peter. "Výstavba lineárnych stochastických modelov časových radov triedy SARIMA – automatizovaný postup." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193057.

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This work concerns the creation of automatized procedure of ARIMA and SARIMA class model choice according to Box-Jenkins methodology and in this connection, also deals with force testing of unit roots and analysis of applying of informatics criteria when choosing a model. The goal of this work is to create an application in the environment R that can automatically choose a model of time array generating process. The procedure is verified by a simulation study. In this work an effect of values of generating ARMA (1,1) model processes parameters is examined, for his choice and power of KPSS test
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Hilmersson, Markus. "A Study Evaluating the Liquidity Risk for Non-Maturity Deposits at a Swedish Niche Bank." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273594.

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Since the 2008 financial crisis, the interest for the subject area of modelling non-maturity deposits has been growing quickly. The area has been widely analysed from the perspective of a traditional bank where customers foremost have transactional and salary deposits. However, in recent year the Swedish banking sector has become more digitized. This has opened up opportunities for more niche banking actors to establish themselves on the market. Therefore, this study aims to examine how the theories developed and previously used in modelling liquidity volumes at traditional banks can be used a
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Samuelsson, Petter, and David Magnusson. "Tidsserieanalys över svensk varuhandel januari 1975 – augusti 2010." Thesis, Uppsala universitet, Statistiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-146035.

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Syftet med denna uppsats är att modellera och prognostisera Sveriges varuexport, varuimport och handelsnetto. Vi använder oss av data från januari 1975 till och med augusti 2010 för respektive serie. Dessa data testas och jämförs i olika ARIMA- och SARIMA-modeller samt skattas även medelst säsongsreningsprogrammet TRAMO/SEATS. För de modeller som bäst passar serierna genomförs därefter in sample- och out of sample-analyser med felmåtten RMSE och MAPE. Modellerna med bäst felmått och som därpå väljs ut för att göra prognoser för serierna till och med augusti 2012 är (3,1,0)x(0,1,1) för export,
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AIDOO, ERIC. "MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS." Thesis, Högskolan Dalarna, Statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4828.

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Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates i
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AIDOO, ERIC. "Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation Rate." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154339.

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In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process.  In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then s
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Norambuena, Ortega Ramón Simón Andrés. "Predicción de Corto Plazo de Potencia Generada en un Aerogenerador Usando Modelo Sarima." Tesis, Universidad de Chile, 2011. http://www.repositorio.uchile.cl/handle/2250/104196.

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El aumento del aporte energético por parte de las centrales eólicas dentro de la matriz de energías renovables no convencionales de Chile, crea la imperiosa necesidad de desarrollar herramientas que ayuden a gestionar el funcionamiento de parques eólicos, y en particular de los aerogeneradores que lo componen, con el fin de hacer más eficiente la integración y manejo en el sistema interconectado. En esta línea, el propósito de este trabajo es desarrollar un modelo predictivo para la potencia generada en un aerogenerador en base a series de tiempo históricas de variables atmosféricas del lugar
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Books on the topic "SARIMA"

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Dexheimer, Verena. Einreisetourismus in Deutschland: Paneldatenanalysen und SARIMA-Prognosen. Lang, 2010.

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Dubois, Vaeronique. Sarina. Andara, 2013.

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Wati, Arena. Sarida. 'K' Pub. & Distributors, 1992.

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James, Miguel. Sarita Sarita tú eres bien bonita. Ediciones Mucuglifo, Dirección Sectorial de Literatura, 2004.

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Budakule, Kirana. Akshara sarita. Bimba Prakasana, 2009.

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Muhammad, Embong. Sumpah Sarimah. Pekan Ilmu Publications Sdn Bhd, 2013.

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Budakule, Kirana. Akshara sarita. Bimba Prakasana, 2009.

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Reduan, Saniah. Surat Sarina. Dewan Bahasa dan Pustaka, Kementerian Pendidikan Malaysia, 1990.

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Ritambhara, Mehta, ed. Aananda-sarita. Gurjar Granth Ratna Karyalaya, 1993.

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Bhatt, Meghnad H. Avadhoot sarika. Rannade Prakashan, 1998.

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Book chapters on the topic "SARIMA"

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Chen, Rong, Rainer Schulz, and Sabine Stephan. "Multiplicative SARIMA models." In Computer-Aided Introduction to Econometrics. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55686-9_5.

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Vogel, Jürgen. "ARIMA- und SARIMA-Modelle." In Prognose von Zeitreihen. Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-06837-0_6.

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Tahyudin, Imam, Berlilana, and Hidetaka Nambo. "SARIMA Model of Bioelectic Potential Dataset." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96292-4_29.

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Xaba, Diteboho, Katleho Makatjane, and Amogelang Senosi. "Prediction Accuracy of SARIMA-STAR-CNE." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73324-6_31.

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Nokeri, Tshepo Chris. "Forecasting Using ARIMA, SARIMA, and the Additive Model." In Implementing Machine Learning for Finance. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7110-0_2.

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Sun, Susu, Xinbo Ai, and Yanzhu Hu. "Emergency Response Technology Transaction Forecasting Based on SARIMA Model." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38460-8_62.

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Batista da Silveira, Tiago, Felipe Augusto Lara Soares, and Henrique Cota de Freitas. "Fast and Efficient Parallel Execution of SARIMA Prediction Model." In Enterprise Information Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75418-1_11.

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Qi, Mingyu, Jingying Chen, and Yunlong Yang. "Optimizing Fresh Warehouse Networks Using MIP and SARIMA Forecasting." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1195-9_11.

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Mouthami, K., N. Yuvaraj, and R. I. Pooja. "Analysis of SARIMA-BiLSTM-BiGRU in Furniture Time Series Forecasting." In Hybrid Intelligent Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_88.

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Zhang, Xiao. "Forecast and Analysis of China’s CPI Based on SARIMA Model." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-030-5_135.

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Conference papers on the topic "SARIMA"

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S, Pramothini, Aakash P, Vanmathi VM, Somasundaram K, and Senthil Kumar Thangavel. "Time Series Forecasting with Hybrid SARIMA." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967979.

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Atanda, Oladayo G., Marion O. Adebiyi, David Adewumi, et al. "Intelligent Sales Forecasting System Using Arima, Sarima, and Xgboost Models." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629780.

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Pratama, Rian Putra, Hanif Fakhurroja, Harry Bangkit, and Dita Pramesti. "Prediction of NPK Fertilizer in Chili Plants Using SARIMA Model." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10751445.

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Dasari, Suresh Babu, Hemanth Suresh Para, and Dheeraz Chaduvula. "Price Forecasting for Vegetables using SARIMA-LSTM and Multitask Learning." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069550.

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Ruales, Johanna, and Manuel Jaramillo. "Medium-term Electric Network Planning Using SARIMA: EERSA Case Study - Ecuador." In 2024 11th International Conference on Power and Energy Systems Engineering (CPESE). IEEE, 2024. https://doi.org/10.1109/cpese62584.2024.10841168.

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Gandhi, Herry Kartika. "Applying Hybrid Forecasting Model SARIMA-SVR for Daily Energy Consumption Data." In 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS). IEEE, 2024. https://doi.org/10.1109/citds62610.2024.10791394.

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Cheng, Tong. "Research on the road traffic accident prediction based on SARIMA-LSTM model." In Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), edited by Xiantao Xiao and Jia Yao. SPIE, 2024. https://doi.org/10.1117/12.3054553.

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Dasari, Suresh Babu, Hemanth Suresh Para, and Dheeraz Chaduvula. "Ensembled Forecasting with Integrated SARIMA and LSTM Models for Tomato Price Prediction." In 2024 International Conference on Cybernation and Computation (CYBERCOM). IEEE, 2024. https://doi.org/10.1109/cybercom63683.2024.10803263.

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Chen, Qifu, Zijie Hong, Xi Chen, and Xiaqiang You. "Forecasting New Energy Vehicle Sales Using Improved BP Neural Network and SARIMA Models." In 2024 4th International Conference on Computer Science and Blockchain (CCSB). IEEE, 2024. http://dx.doi.org/10.1109/ccsb63463.2024.10735635.

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Frias-Telesca, Luana, Nelcy N. Atehortua-Sanchez, and Juan Sebastian Blandon. "Forecasting seasonal rainfall in Uruguay: A comprehensive analysis using ARIMA and SARIMA models." In 2024 IEEE URUCON. IEEE, 2024. https://doi.org/10.1109/urucon63440.2024.10850020.

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Reports on the topic "SARIMA"

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Akbari, Chirag, Ninad Gore, and Srinivas Pulugurtha. Understanding the Effect of Pervasive Events on Vehicle Travel Time Patterns. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2319.

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The COVID-19 pandemic has disrupted daily activities and travel patterns, affecting personal and commercial trips. This study investigates the effect of different stages of the pandemic on travel time patterns. Eighty-six geographically distributed links (sections of road) in Mecklenburg County and Buncombe County, North Carolina, were selected for analysis. The selected links accounted for the variation in road geometry, land use, and speed limit. Travel time data for three years (i.e., 2019, 2020, and 2021) were extracted from a private data source at 5-min intervals. Travel time reliability
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Roldán-Ferrín, Felipe, and Julián A. Parra-Polania. ENHANCING INFLATION NOWCASTING WITH ONLINE SEARCH DATA: A RANDOM FOREST APPLICATION FOR COLOMBIA. Banco de la República, 2025. https://doi.org/10.32468/be.1318.

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This paper evaluates the predictive capacity of a machine learning model based on Random Forests (RF), combined with Google Trends (GT) data, for nowcasting monthly inflation in Colombia. The proposed RF-GT model is trained using historical inflation data, macroeconomic indicators, and internet search activity. After optimizing the model’s hyperparameters through time series cross-validation, we assess its out-of-sample performance over the period 2023–2024. The results are benchmarked against traditional approaches, including SARIMA, Ridge, and Lasso regressions, as well as professional forec
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สุจริต, จิรศักดิ์. หอยทากบกในพื้นที่โครงการอนุรักษ์พันธุกรรมพืชอันเนื่องมาจากพระราชดำริฯ สมเด็จพระเทพรัตนราชสุดาฯ สยามบรมราชกุมารี: พื้นที่จุฬาลงกรณ์มหาวิทยาลัย จังหวัดน่าน : รายงานผลการดำเนินงาน. จุฬาลงกรณ์มหาวิทยาลัย, 2018. https://doi.org/10.58837/chula.res.2018.73.

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การศึกษาและสำรวจหอยทากบกในพื้นที่ของโครงการอนุรักษ์พันธุกรรมพืช อันเนื่องมาจากพระราชดำริ ฯ พระราชดำริฯ สมเด็จพระเทพรัตนราชสุดาฯ สยามบรมราชกุมารี: พื้นที่จุฬาลงกรณ์มหาวิทยาลัย จังหวัดน่าน ในสภาพพื้นที่ธรรมชาติ มุ่งเน้นที่กลุ่มหอยขัดเปลือกที่พบได้ทั่วไปวงศ์ Ariophantide โดยมี หอยหอยขัดเปลือกที่จัดได้ว่าพบได้ทั่วไปและมีการแพร่กระจายกว้างทั่วประเทศไทย 2 สปีชีส์ คือหอย ทากสยาม Cryptozona siamensis และหอยขัดเปลือกธรรมดา Sarika resplenden จากเอกสารที่ รายงานเกี่ยวกับหอยในวงศ์นี้ในพื้นที่ใกล้เคียงกับประเทศไทยและมีแนวโน้มที่จะพบวงศ์หอยขัดเปลือก ในประเทศไทย มีอยู่ประมาณ 60 สปีชีส์ โดยในพื้นที่ อพ.สธ. ทั
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สุจริต, จิรศักดิ์. อนุกรมวิธานของหอยขัดเปลือกวงศ์ Ariophantidae ในพื้นที่โครงการอนุรักษ์พันธุกรรมพืช อันเนื่องมาจากพระราชดำริฯ สมเด็จพระเทพรัตนราชสุดาฯ สยามบรมราชกุมารี : หมู่เกาะแสมสาร จังหวัดชลบุรี และเขาวังเขมร จังหวัดกาญจนบุรี : รายงานผลการดำเนินงาน. จุฬาลงกรณ์มหาวิทยาลัย, 2014. https://doi.org/10.58837/chula.res.2014.37.

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การศึกษาและสำรวจหอยทากบกในพื้นที่ของโครงการอนุรักษ์พันธุกรรมพืช อันเนื่องมาจากพระราชดำริฯ ใน 2 บริเวณได้แก่ หมู่เกาะแสมสาร จังหวัดชลบุรี และ พื้นที่เขาวังเขมร-ช่องเขาขาด อำเภอไทรโยค จังหวัด กาญจนบุรี พบหอยหอยขัดเปลือกวงศ์ Ariophantidae ที่จัดได้ว่าพบได้ทั่วไปและมีการแพร่กระจายกว้างทั่วประเทศไทย 2 ชนิดคือหอยทากสยาม Cryptozona siamensis และหอยขัดเปลือกธรรมดา Sarika resplenden จากเอกสารที่รายงานเกี่ยวกับหอยในวงศ์นี้ในพื้นที่ใกล้เคียงกับประเทศไทยและมีแนวโน้มที่จะพบวงศ์หอยขัดเปลือกในประเทศไทย มีอยู่ประมาณ 60 ชนิด โดยในพื้นที่ อพ.สธ. ทั้งในภาคตะวันออกและ ภาคตะวันตกพบแล้วประมาณ 25 ชนิด แต่ส่วนใหญ่ไม่
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Pater, Larry. SARNAM Noise Impact Software. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada607031.

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Rueda, Diana, and Eylla Laire Gutierrez. Gender, Microentrepreneurship, Human Flourishing: Exploring the Experiences of Women Sari-sari Store Owners toward Inclusive Growth. Philippine Institute for Development Studies, 2025. https://doi.org/10.62986/dp2024.50.

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For years, women’s involvement in entrepreneurial activities has been widely advocated as part of economic development agendas. This is evidenced by numerous initiatives, projects, and roadmaps that promote women's integration into economic activities through entrepreneurship. Underpinning these efforts is the assumption that women's active participation in economic activities leads to improved well-being and empowerment. While this is an essential step toward improving women’s conditions, the realities are more complex. Beyond the economic contributions of such entrepreneurial activities, oth
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Rueda, Diana, and Eylla Laire Gutierrez. Empowerment and Human Flourishing of Women Sari-Sari Store Owners in the Philippines. Philippine Institute for Development Studies, 2025. https://doi.org/10.62986/pn2025.06.

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This Policy Note highlights the vital role of women-owned sari-sari stores in shaping the Philippine socioeconomic landscape. Using the Participation-Empowerment framework and the Human Flourishing Index, this study assesses how these microenterprises contribute significantly to women’s empowerment and human flourishing while fostering social capital and community engagement. Based on survey data from 311 female store owners in Central Luzon and Metro Manila, the research examines their empowerment across various dimensions. Findings indicate that women entrepreneurs experience a sense of prid
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Chakraborty, Srijani. The Dawn of RNA Therapeutics. Spring Library, 2020. http://dx.doi.org/10.47496/sl.blog.19.

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Sopori, Mohan L. Neuroimmune Effects of Inhaling Low Dose Sarin. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada442264.

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Kisner, Lisa. SARRA Service Academies & ROTC Research Associates. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1825375.

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