The hybrid PROPHET-SVR approach for forecasting product time series demand with seasonality

Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm

(2021) Ahmet Kara   EXPERT SYSTEMS WITH APPLICATIONS

A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption

(2020) Fazil Kaytez   ENERGY

Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion

(2020) Mahdi Abolghasemi et al.   COMPUTERS & INDUSTRIAL ENGINEERING

An optimized model using LSTM network for demand forecasting

(2020) Hossein Abbasimehr et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river

(2020) Thi-Thu-Hong Phan et al.   ADVANCES IN WATER RESOURCES

Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors

(2020) Zheng-Xin Wang et al.   ENERGY

A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting

(2020) Mohammad-Rasool Kazemzadeh et al.   ENERGY

A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine

(2020) Che Liu et al.   APPLIED ENERGY

An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China

(2020) Wuyong Qian et al.   ENERGY

Forecasting of customer demands for production planning by local k-nearest neighbor models

(2020) Mirko Kück et al.   INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS

A grey seasonal least square support vector regression model for time series forecasting

(2020) Weijie Zhou et al.   ISA TRANSACTIONS

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

(2020) João N.C. Gonçalves et al.   DECISION SUPPORT SYSTEMS

A novel method for time series prediction based on error decomposition and nonlinear combination of forecasters

(2020) Wei Chen et al.   NEUROCOMPUTING

Forecasting daily stock trend using multi-filter feature selection and deep learning

(2020) Anwar Ul Haq et al.   EXPERT SYSTEMS WITH APPLICATIONS

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

(2020) Fotios Petropoulos et al.   INTERNATIONAL JOURNAL OF FORECASTING

A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting

(2019) Priyanka Singh et al.   ENERGY

Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

(2019) Ümit Çavuş Büyükşahin et al.   NEUROCOMPUTING

Passenger demand forecasting in scheduled transportation

(2019) Nilabhra Banerjee et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China

(2019) Tuo Xie et al.   JOURNAL OF HYDROLOGY

A support vector machine for model selection in demand forecasting applications

(2018) Marco A. Villegas et al.   COMPUTERS & INDUSTRIAL ENGINEERING

Day-of-week and seasonal patterns of PM2.5 concentrations over the United States: Time-series analyses using the Prophet procedure

(2018) Naizhuo Zhao et al.   ATMOSPHERIC ENVIRONMENT

Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions

(2018) Niematallah Elamin et al.   ENERGY

Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach

(2018) Shuojiang Xu et al.   TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

Enriching demand forecasts with managerial information to improve inventory replenishment decisions: Exploiting judgment and fostering learning

(2017) Yacine Rekik et al.   EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology

(2017) Gang Xie et al.   TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

An improved demand forecasting method to reduce bullwhip effect in supply chains

(2013) Sanjita Jaipuria et al.   EXPERT SYSTEMS WITH APPLICATIONS

ANN-based residential water end-use demand forecasting model

(2012) Christopher Bennett et al.   EXPERT SYSTEMS WITH APPLICATIONS

Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks

(2011) Lara Khansa et al.   DECISION SUPPORT SYSTEMS

Application of chaotic ant swarm optimization in electric load forecasting

(2010) Wei-Chiang Hong   ENERGY POLICY

Demand forecasting errors in industrial context: Measurement and impacts

(2008) Annastiina Kerkkänen et al.   INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS