Worldwide, influenza is estimated to result in approximately 3 to 5 million annual cases of severe illness and approximately 250,000 to 500,000 deaths. We need an accurate time-series model to predict the number of influenza patients. Although time-series models with different time lags as feature spaces could lead to varied accuracy, past studies simply adopted a time lag in their models witho...

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2...

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With increase installed WWTPs worldwide, modeling and forecast their energy have become a critical factor in WWTP design meet environmental economic requirements. The accurate swift forecasting soft-sensors are not only supportive daily electric financial budgeting by practitioners on micro-scale, but also b...

Februari 2020 merupakan waktu awal warga Indonesia didiagnosa positif Covid-19. Hingga kini, penyakit yang dikarenakan oleh Virus Corona ini belum mereda, bahkan dinyatakan sebagai Pandemi Global. Tujuan penelitian adalah mencari model prediksi Time Series untuk jumlah kasus Covid-19 di salah satu kota dengan infeksi terbsesar yaitu Jakarta. Penelitian menggunakan data dari Open Data Jakarta re...

Time series modelling and forecasting is a vibrant research field that had attracted the interest of scientific community in recent decades. Forecasts agricultural prices are proposed to be useful for farmers, governments, policy makers agribusiness industries. In this study, an effort made compare capabilities well-known linear Auto Regressive Integrated Moving Average (ARIMA) models, Delay Ne...

Energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. However, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. Accordingly, in this paper a new strategy is proposed for electricity price forecast. The forecast strategy includes Wavelet Transform (WT...

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

Abstract Auto-regressive integrated moving average (ARIMA) assessment of traffic noise was conducted on different routes in Port Harcourt, Nigerian metropolis. This achieved by measuring the various asphalt flexible and concrete rigid pavement structures with a meter for sound measurement regards to volume traffic, vehicle movement rate, location away from midpoint highway. The peak obtained at...