The Correlation of the Data Structure with Demand Forecasting Accuracy

2020 ◽  
Vol 16 (5) ◽  
pp. 467-483
Author(s):  
Sung-Yong Yoo ◽  
Min-Young Park ◽  
Yoon-Taek Kong
Keyword(s):  
Data Structure ◽  
Demand Forecasting ◽  
Forecasting Accuracy

Improving Demand Forecasting Accuracy for Cultural Facilities Using Gravity Model

2020 ◽  
Vol 24 (2) ◽  
pp. 121-163
Author(s):  
Yun Ju Ham ◽  
Yun Jun Kim ◽  
Geun Seok Hong
Keyword(s):  
Gravity Model ◽  
Demand Forecasting ◽  
Forecasting Accuracy

scholarly journals Nowcasting Regional GDP: The Case of the Free State of Saxony

2015 ◽  
Vol 66 (1) ◽  
pp. 71-98
Author(s):  
Steffen R. Henzel ◽  
Robert Lehmann ◽  
Klaus Wohlrabe
Keyword(s):  
Data Structure ◽  
Manufacturing Sector ◽  
Regional Level ◽  
Free State ◽  
Large Set ◽  
Regional Survey ◽  
Forecasting Accuracy ◽  
Leading Indicator ◽  
Information Sets ◽  
Survey Results

Abstract We tackle the nowcasting problem at the regional level, using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

Pooling in Tourism Demand Forecasting

2018 ◽  
Vol 58 (7) ◽  
pp. 1161-1174 ◽  
Author(s):  
Wen Long ◽  
Chang Liu ◽  
Haiyan Song
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Demand Forecasting ◽  
Spillover Effects ◽  
Ordinary Least Squares ◽  
Spatial Effects ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Demand Models ◽  
Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

scholarly journals Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Richard T.R. Qiu ◽  
Anyu Liu ◽  
Jason L. Stienmetz ◽  
Yang Yu
Keyword(s):  
Social Media ◽  
Time Series ◽  
Hospitality Industry ◽  
Demand Forecasting ◽  
Time Series Model ◽  
Social Media Data ◽  
Forecasting Accuracy ◽  
Social Unrest ◽  
Content Type ◽  
Media Data

Purpose The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example. Design/methodology/approach Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy. Findings Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The finding of this research is that the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises. Originality/value The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.

scholarly journals Combining Qualitative and Time Series Forecasting to Increase the Forecasting Accuracy for Instant Noodle Sales in Thailand

2016 ◽  
Vol 3 (3) ◽  
pp. 1
Author(s):  
Teerada Khamphinit ◽  
Pornthipa Ongkunaruk
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Time Series Forecasting ◽  
Forecasting Accuracy ◽  
Sales Data ◽  
Average Percentage ◽  
Sales Forecast ◽  
Instant Noodle ◽  
Instant Noodles

<p>Demand forecasting is very important for the planning process. The forecast accuracy affects the efficiency of the procurement, production and delivery processes. Our research has the objective of increasing the sales forecasting accuracy of instant noodles for a case study company in Thailand. Many factors affect the sales of instant noodles, such as promotion, other commodities’ prices, national disaster and production capacity. Thus, we collected historical monthly sales data, analysed the data and their pattern and considered whether the data were irregular due to those factors. After obtaining the forecast data, data intervention by adjustment of the irregular effects was performed in accordance with our experience and judgement. Next, we used the predictor function in the Crystal Ball software to determine the best time series forecasting method for actual and adjusted sales data. Then, we verified the result with the actual sales data for one year. The result showed that the adjustment could increase the sales forecast accuracy by 46.14%, 22.53% and 56.42% for products A, B and C, respectively. In summary, the mean average percentage sales forecast error after adjustment was 6.48%–11.62%, which is better than the current method of forecasting based on experts.  </p><p><strong>Keywords</strong>: Instant Noodle; Intervention; Qualitative Forecasting; Sales Adjustment; Time Ser ies Forecasting </p>

Decomposition Methods for Tourism Demand Forecasting: A Comparative Study

2021 ◽  
pp. 004728752110361
Author(s):  
Chengyuan Zhang ◽  
Mingchen Li ◽  
Shaolong Sun ◽  
Ling Tang ◽  
Shouyang Wang
Keyword(s):  
Decomposition Method ◽  
Demand Forecasting ◽  
Decomposition Methods ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Mode Decomposition ◽  
Forecasting Performance ◽  
Intrinsic Complexity ◽  
Comprehensive Comparison

Decomposition methods are extensively used for processing the complex patterns of tourism demand data. Given tourism demand data’s intrinsic complexity, it is critical to theoretically understand how different decomposition methods provide solutions. However, a comprehensive comparison of decomposition methods in tourism demand forecasting is still lacking. Hence, this study systematically investigates the forecasting performance of decomposition methods in tourism demand. Nine popular decomposition methods and six forecasting methods are employed, and their forecasting performance is compared. With Hong Kong visitor arrivals from eight major sources as a sample, three main conclusions are obtained from empirical results. First, all the decomposition methods generally outperform benchmark at all horizons, in both the level and directional forecasting. Second, decomposition methods can be divided into four categories based on forecasting accuracy. Finally, variational mode decomposition method is consistently superior to other eight decomposition methods and can provide the best forecasts in all cases.

scholarly journals Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 10 ◽  
Author(s):  
Rabiya Khalid ◽  
Nadeem Javaid ◽  
Fahad A. Al-zahrani ◽  
Khursheed Aurangzeb ◽  
Emad-ul-Haq Qazi ◽  
...  
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Proposed Model ◽  
Multiple Variables ◽  
Long Short Term Memory

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

scholarly journals Tourism Demand Modelling and Forecasting: A Review of Literature

2021 ◽  
Vol 10(4) (10(4)) ◽  
pp. 1370-1393
Author(s):  
Musonera Abdou ◽  
Edouard Musabanganji ◽  
Herman Musahara
Keyword(s):  
Time Series ◽  
Spatial Regression ◽  
Demand Forecasting ◽  
Tourism Demand ◽  
New Developments ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Modelling And Forecasting ◽  
Forecasting Techniques

This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.

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