The Housing Price Forecasting and the Outbreak of the Financial Crisis: Evidence of the Arima Model

2010 ◽  
Author(s):  
Yi-Chi Chen ◽  
Wei-Choun Yu
Keyword(s):  
Financial Crisis ◽  
Arima Model ◽  
Housing Price ◽  
Price Forecasting

scholarly journals Price Forecasting of Mango in Varanasi Market of Uttar Pradesh

2018 ◽  
Vol 6 (2) ◽  
pp. 218-224 ◽  
Author(s):  
Ravishankar Pardhi ◽  
Rakesh Singh ◽  
Ranjit Kumar Paul
Keyword(s):  
Arima Model ◽  
Uttar Pradesh ◽  
The State ◽  
Percentage Error ◽  
Price Forecasting ◽  
Harvest Management ◽  
Alternate Bearing ◽  
Agricultural Produce ◽  
One Step ◽  
Forecasting Analysis

The study had been made to forecast the price of mango using ARIMA model in one of the major markets of Uttar Pradesh as the state ranks first position in production of mango in India. Varanasi market was selected purposively on the basis of second highest arrival market of mango in the state. Using ARIMA methodology on the monthly prices of mango collected from the Agricultural Produce Market Committee (APMC), Varanasi for the year 1993 to 2015. As the mango fruit having property of alternate bearing, only six month data from March to August was available in the market and accordingly had been used for forecasting analysis using E-views 7 software. The results revealed that the price in selected market was found to be highest during the start of the season using ARIMA (1,0,6) model, confirming the validity of model through Mean Absolute Percentage Error (MAPE). The MAPE was found to be less than 10 per cent for one step ahead forecast of year 2015. Forecasted price for the month of March was almost double than the price of other months. It indicates the necessity of adopting pre and post harvest management technologies for getting the benefit over increase in prices.

Influence of Modeling Methods for Housing Price Forecasting

2013 ◽  
Vol 798-799 ◽  
pp. 885-888
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Housing Price ◽  
Correlation Coefficients ◽  
Partial Least Square ◽  
Least Square ◽  
Percentage Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Modeling Methods ◽  
Variables Selection

In this work, we estimate Yunnan housing price from 1999 to 2009. Firstly, we analyze the correlation coefficients between housing price and characteristic variables, identify the characteristic variables. Then, we build the forecasting model using four techniques, support vector regression (SVR), radial basis function neural network (RBFNN), partial least square (PLS) and multiple regression analysis (MRA), based on whole variables and characteristic variables. The results show that PLS technique is the best one for housing price forecasting. Its mean absolute percentage error (MAPE) is only 2.45%. SVR and RBFNN are better techniques to obtain a satisfactory forecasting result with almost 5% MAPE. Furthermore, the performance of MRA and SVR can be obviously improved through variables selection.

scholarly journals The ARIMA Box-Jenkins Method has been used to Predict the Price of Large Curly Red Chilis

2021 ◽  
pp. 89
Author(s):  
Yustirania Septiani ◽  
Vinca Ayu Setyowati
Keyword(s):  
Arima Model ◽  
Economic Value ◽  
Price Forecasting ◽  
Market Demand ◽  
Model Based

Chili is one of the potential commodities based on market demand and high economic value. The price of chili has fluctuated every month so that this commodity contributes to inflation in food that can affect overall general inflation. Thus, an analysis of forecasting prices for large curly red chili is needed so thar people and farmers do not need to worry and can prepare for future risks. Price forecasting in this study uses the Box-Jenkins ARIMA method. The data used is the price of lare curly red chili prices from December 2015 to April 2020. The data to be analyzed is then made into several forms of the ARIMA model and one will be chosen as the best ARIMA model. Based on the results of the study, ARIMA (1,1,3) is the best model. Thus the forecast results obtained for the price of large curly red chili in Magelang City from May 2020 to February 2021. With this research it is expected ti be able to assist the Depasrtment of Industry and Trade of Magelang City in making decisions related to the price of lare curly red chilli which fluctuates every year.

Cumin Price Forecasting in Rajasthan: ARIMA Model

2016 ◽  
Vol 12 (1a) ◽  
pp. 51
Author(s):  
Vinod Kumar Verma ◽  
S.S. Jheeba ◽  
Hemant Sharma ◽  
Pradeep Kumar
Keyword(s):  
Arima Model ◽  
Price Forecasting

Lead Price Forecasting Based on ARIMA Model

2012 ◽  
Vol 488-489 ◽  
pp. 1582-1586 ◽  
Author(s):  
Lin Lin Lu ◽  
Xin Ma ◽  
Ya Xuan Wang ◽  
Gen Bo Yu
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Arima Model ◽  
High Accuracy ◽  
Economic Index ◽  
Price Forecasting ◽  
Price Data ◽  
Lead Price ◽  
Optimization Study

The time series ARIMA (3,1,4) model was established, which is taken into use of price forecasting. Then the forecasted price was applied to mining technical and economic index optimization study. Lead prices could be reliably predicted by time series ARIMA model, which had a high accuracy and the percentage of prediction error was 2.97% on average. It can solve the problems of the price data lag in the study of mine economic index optimization very well.

Sign in / Sign up

Export Citation Format

Share Document