Modeling and Forecasting of Housing Prices in Tianjin Based on Time Series

2021 ◽  
Vol 10 (04) ◽  
pp. 583-592
Author(s):  
正才 李
Keyword(s):  
Time Series ◽  
Housing Prices ◽  
Modeling And Forecasting

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

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