scholarly journals A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction

2020 ◽  
Vol 12 (4) ◽  
pp. 11
Author(s):  
Chuanhua Wei ◽  
Chenping Du ◽  
Nana Zheng
Keyword(s):  
Spatial Data ◽  
Time Series Data ◽  
House Prices ◽  
Housing Price ◽  
Combination Method ◽  
Series Data ◽  
Forecast Combination ◽  
Combination Methods ◽  
Seminal Article ◽  
Combination Approach

Forecast combination has been widely applied in various fields since the seminal article of Bates and Granger (1969). However, these research were focused only on time series data. Few study focus on the spatial data, this paper proposes a novel adaptive spatial forecast combination method with varying weights based on the geographically weighted regression technique. Finally, the proposed method is applied to the Boston house prices prediction, and the results indicate that our procedure performs better than the other forecast combination methods.

scholarly journals An Empirical Mode-Spatial Model for Environmental Data Imputation

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 63 ◽  
Author(s):  
Benjamin Nelsen ◽  
D. Williams ◽  
Gustavious Williams ◽  
Candace Berrett
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Missing Values ◽  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Data Imputation ◽  
Accurate Data ◽  
Target Station ◽  
Periodic Components

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

scholarly journals PENGARUH KORUPSI, DEMOKRASI DAN POLITIK TERHADAP KEMISKINAN DI DELAPAN NEGARA ASEAN DENGAN PERTUMBUHAN EKONOMI SEBAGAI VARIABEL MODERASI

2019 ◽  
Vol 1 (3) ◽  
pp. 845
Author(s):  
Yolanda Yolanda
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Predictor Variable ◽  
Combination Method ◽  
Annual Reports ◽  
Series Data ◽  
Transparency International ◽  
Negative Effect ◽  
Asean Countries ◽  
The Relationship

This study aims the influence of corruption, democracy and politics on poverty in ASEAN countries with economic growth as a moderating variable. The method used is using the panel regression model. This data uses a combination method between time series data from 2013 - 2016 and a cross section consisting of 8 countries. Data obtained from World Bank annual reports, Transparency International and Freedom House. The results of this study indicate that (1) Corruption Perception Index (CPI) has a significant and negative effect on poverty, meaning that if the CPI increases then poverty will decrease (2) Democracy has no significant and negative effect on poverty. This means that if democracy increases, poverty will decrease (3) Politics has a significant and negative effect on poverty, meaning that if politics increases, poverty will decrease (4) Economic growth has a significant and positive effect on poverty, meaning if economic growth increases then poverty will decline (3) Economic growth unable to moderate the relationship between corruption, democracy and politics towards poverty in 8 ASEAN countries. Economic growth as an interaction variable is a predictor variable (Predictor Moderate Variable), which means that economic growth is only an independent variable.

Municipal changes in Slovakia. The evidence from spatial data.

2020 ◽  
Vol 11 (1) ◽  
pp. 58-72
Author(s):  
Martin MARIS
Keyword(s):  
Spatial Data ◽  
Time Series Data ◽  
Population Sample ◽  
Series Data ◽  
Annual Growth Rate ◽  
The West ◽  
Compound Annual Growth Rate ◽  
Fast Growing ◽  
The North ◽  
Statistical Criteria

The main objective of the paper is to examine the evolution of spatial patterns of settlement network in Slovakia as a result of population rearrangement among municipalities based on time series data of 1993 - 2017. The objects of the research are municipalities, which during the searched period recorded unusual fast population growth or decline, far exceeding the chosen parameter of the population sample. The primary population sample consists of 2919 municipalities. The experimental samples consist of 563 of fast-growing municipalities and 413 of fast-declining municipalities, based on the chosen statistical criteria, what is the compound annual growth rate. The results have shown that fast-growing municipalities are predominantly situated on the West, surrounding the Bratislava agglomeration, on the North and the East surrounding the Kosice metropolis. Generally, they tend to cluster around the cities on the district and regional levels. Fast-declining municipalities predominantly situated in the Middle, along the Hungarian, Polish, and Ukrainian border on the South and the East of the country, respectively.

scholarly journals Optimal forecasting accuracy using Lp-norm combination

METRON ◽  
2021 ◽  
Author(s):  
Massimiliano Giacalone
Keyword(s):  
Real Data ◽  
Optimization Techniques ◽  
Combination Method ◽  
Forecast Combination ◽  
Forecasting Accuracy ◽  
Lp Norm ◽  
Combination Methods ◽  
Historical Series ◽  
Non Gaussian ◽  
Standard Regression

AbstractA well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451–468, 1969). The only case in which no improvements are possible is when one of the single forecasts is already the optimal one in terms of MSE. The kinds of combination methods are various, ranging from the simple average (SA) to more robust methods such as the one based on median or Trimmed Average (TA) or Least Absolute Deviations or optimization techniques (Stock and Watson in J Forecast 23(6):405–430, 2004). Standard regression-based combination approaches may fail to get a realistic result if the forecasts show high collinearity in several situations or the data distribution is not Gaussian. Therefore, we propose a forecast combination method based on Lp-norm estimators. These estimators are based on the Generalized Error Distribution, which is a generalization of the Gaussian distribution, and they can be used to solve the cases of multicollinearity and non-Gaussianity. In order to demonstrate the potential of Lp-norms, we conducted a simulated and an empirical study, comparing its performance with other standard-regression combination approaches. We carried out the simulation study with different values of the autoregressive parameter, by alternating heteroskedasticity and homoskedasticity. On the other hand, the real data application is based on the daily Bitfinex historical series of bitcoins (2014–2020) and the 25 historical series relating to companies included in the Dow Jonson, were subsequently considered. We showed that, by combining different GARCH and the ARIMA models, assuming both Gaussian and non-Gaussian distributions, the Lp-norm scheme improves the forecasting accuracy with respect to other regression-based combination procedures.

scholarly journals Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4660
Author(s):  
Fa Zhao ◽  
Guijun Yang ◽  
Hao Yang ◽  
Yaohui Zhu ◽  
Yang Meng ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Vegetation Index ◽  
Time Series Data ◽  
Short Term Memory ◽  
Combination Method ◽  
Series Data ◽  
Medium Term ◽  
Short Term ◽  
Deep Recurrent Neural Network

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction.

Impact of macroeconomic indicators on housing prices

2019 ◽  
Vol 12 (6) ◽  
pp. 1055-1071 ◽  
Author(s):  
Satish Mohan ◽  
Alan Hutson ◽  
Ian MacDonald ◽  
Chung Chun Lin
Keyword(s):  
Price Index ◽  
Time Series Data ◽  
Statistical Significance ◽  
Housing Prices ◽  
Consumer Price Index ◽  
New York State ◽  
Housing Price ◽  
Series Data ◽  
Content Type ◽  
Macroeconomic Indicators

Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

scholarly journals A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7109
Author(s):  
Chengying Zhao ◽  
Xianzhen Huang ◽  
Yuxiong Li ◽  
Muhammad Yousaf Iqbal
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Spatial Data ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Useful Life ◽  
Lstm Network ◽  
Double Channel

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

scholarly journals Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3569 ◽  
Author(s):  
Phathutshedzo Mpfumali ◽  
Caston Sigauke ◽  
Alphonce Bere ◽  
Sophie Mulaudzi
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Prediction Interval ◽  
Combination Method ◽  
Support Vector ◽  
Forecast Combination ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Stochastic Gradient Boosting ◽  
Combination Methods

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

scholarly journals Negative equity in Ireland: estimates using loan-level data

2014 ◽  
Vol 7 (3) ◽  
pp. 327-344 ◽  
Author(s):  
David Duffy ◽  
Niall O’Hanlon
Keyword(s):  
Design Methodology ◽  
Time Series Data ◽  
House Prices ◽  
Age Groups ◽  
Series Data ◽  
Data Set ◽  
Content Type ◽  
Equity Analysis ◽  
Level Data ◽  
Younger Age

Purpose – This paper aims to, using a unique loan-level data set, show the extent to which negative equity in Ireland is concentrated in younger age groups. The sharp decline in house prices since 2007 has led to the emergence of widespread negative equity in Ireland. However, little is known about the type of borrower experiencing negative equity. Design/methodology/approach – This paper uses a unique data set that, for a large sample of mortgages, provides details on both the characteristics of the borrowers and their mortgages. Using this data set, the paper estimates the incidence of negative equity by analysing loans taken out to purchase a primary residence in the period 2005-2012. Findings – The analysis finds the situation in Ireland to be much more severe than that being experienced in other housing market downturns at present, with 64 per cent of borrowers in the period 2005-2012 experiencing negative equity. Analysis by age gives rise to concern, with the majority of those in negative equity aged under 40 years. The paper also points to the large wealth loss experienced by Irish households, in the order of 43 billion, as a result of the fall in property values. Originality/value – The paper is one of the first using loan-level time-series data in Ireland. It highlights the growth in negative equity during the crisis and the extent to which it is concentrated in the younger age groups. It also provides an estimate of the loss in wealth suffered by all households due to the fall in Irish house prices.

Modelling the supply of new residential construction in Aberdeen, UK

2014 ◽  
Vol 7 (3) ◽  
pp. 346-362 ◽  
Author(s):  
Anthony Owusu-Ansah
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
House Prices ◽  
Local Level ◽  
Series Data ◽  
Local Markets ◽  
Content Type ◽  
Time On The Market ◽  
Basic Model ◽  
Housing Starts

Purpose – The purpose of this paper is to use local-level time series data to examine the determinants of housing starts and the price elasticity of supply for the Aberdeen local housing market. Design/methodology/approach – Seven time series models are used in the analysis. The basic model treats housing starts as a function of the changes of current and lagged house prices, interest rate and construction cost. The other six models which are extensions of the basic model include other variables like time on the market, planning constraints and future expectations. Findings – It is found that the local variables – changes in house prices, time on the market, planning regulation, lagged stock and lagged and future housing starts – are the main factors that influence new residential construction in Aberdeen. None of the national variables is significant, confirming the importance of limiting housing market analysis to the local level. The price elasticity of supply estimated is in the range of 2.0 to 3.2 for housing starts and 0.01 to 0.02 for housing stock. These estimates are higher than most of the elasticities for the other UK local markets. Originality/value – There is the need to better understand the supply of housing at the various local housing markets. Unfortunately, however, most housing supply studies use national data. Because national data are aggregation of local data, using national studies results for local markets may be uninformative. Also, the few existing local studies use typically cross-section data or at least time series over relatively short time spans. This paper makes an effort to use quarterly time series data over a 25-year period for a local market and also include a planning variable which is different from local markets and often ignored in national or regional studies.

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