Improving efficacy of library Services: ARIMA modelling for predicting book borrowing for optimizing resource utilization

Book borrowing is a key service in libraries. Library users frequently visit the library for borrowing books compared to other library services. To predict book-borrowing service in a college library, Auto Regressive Integrated Moving Average (ARIMA) model has been developed from the data pertaining to book borrowing during the year 1998 to 2013. The study found that the number of books borrowed one month and twelve months earlier could estimate the number of books borrowed in a month. The study used a fitted model for predicting book borrowing for the year 2014 by two alternative approaches: 12-steps ahead versus 1-step ahead. The calculations show that there was no significant difference (P=0.928; Wilcoxon signed rank test) between 1-step and 12-steps ahead approach for predicting book borrowing. However, the Root Mean Squared Error (RMSE) in 1-step ahead approach (109.57) was lower than 12-steps approach (131.33). The study findings indicate that ARIMA models are useful for monitoring book borrowing in institutional libraries. Furthermore, these models can predict library usage trends

Improving efficacy of library Services: ARIMA modelling for predicting book borrowing for optimizing resource utilization

Book borrowing is a key service in libraries. Library users frequently visit the library for borrowing books compared to other library services. To predict book-borrowing service in a college library, Auto Regressive Integrated Moving Average (ARIMA) model has been developed from the data pertaining to book borrowing during the year 1998 to 2013. The study found that the number of books borrowed one month and twelve months earlier could estimate the number of books borrowed in a month. The study used a fitted model for predicting book borrowing for the year 2014 by two alternative approaches: 12-steps ahead versus 1-step ahead. The calculations show that there was no significant difference (P=0.928; Wilcoxon signed rank test) between 1-step and 12-steps ahead approach for predicting book borrowing. However, the Root Mean Squared Error (RMSE) in 1-step ahead approach (109.57) was lower than 12-steps approach (131.33). The study findings indicate that ARIMA models are useful for monitoring book borrowing in institutional libraries. Furthermore, these models can predict library usage trends

Regional Forecasting with Support Vector Regressions: The Case of Spain

This study attempts to assess the forecasting accuracy of Support Vector Regression (SVR) with regard to other Artificial Intelligence techniques based on statistical learning. We use two different neural networks and three SVR models that differ by the type of kernel used. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian kernel shows the best forecasting performance. The best predictions are obtained for longer forecast horizons, which suggest the suitability of machine learning techniques for medium and long term forecasting

Peramalan Harga Pasar Telur Ayam Ras di Kota Malang dengan Menggunakan Metode "SVR-PSO"

Telur ayam ras menjadi salah satu sumber protein favorit masyarakat karena harganya cukup terjangkau dibandingkan sumber protein hewani lainnya yang dijual bebas di pasaran. Permasalahan utama yang dihadapi konsumen adalah fluktuasi harga pasar telur ayam ras di Kota Malang, ada kalanya harga naik dan ada kalanya harga turun. Hal ini akan menjadi masalah jika harga pasar naik tajam dari harga pada bulan-bulan sebelumnya. Pada penelitian ini dibuat sistem yang mampu meramalkan harga pasar dengan menggunakan metode Support Vector Regression (SVR) untuk melakukan peramalan dan metode Particle Swarm Optimization (PSO) untuk mengoptimasi parameter SVR. Proses optimasi terdiri dari 4 tahapan utama, yaitu tahapan normalisasi, tahapan pelatihan SVR, tahapan PSO, dan tahapan pengujian SVR. Pada pengujian SVR didapatkan nilai MAPE terkecil yaitu sebesar 6,2186% dan pada pengujian SVR-PSO didapatkan nilai MAPE terkecil sebesar 1,8840%