Price Index Forecasting Research Articles

Consumer Price Index Forecasting in Turkey: A Comparison of Deep Learning and Machine Learning Approaches

Accordingly, different deep learning and machine learning models such as long- and short-term memory, temporal recurrent units, random forests, artificial neural networks, and K-nearest neighbors are used for CPI forecasting. The prediction performances of the models on the test data were evaluated with RMSE, MSE, MAE, MAPE, and R^2 error statistics. The results show that the Gateway Recurrent Unit model outperforms the Long and Short Term Memory, Random Forest, Neural Network, and K-Nearest Neighbors models. Compared to the other four models, the RMSE, MSE, MAE, MAPE, and R^2 values performed better in the recurrent unit model. In addition, it has been observed that deep learning and machine learning models can be used effectively in the field of inflation in consumer price index forecasting. These results provide an effective method of CPI forecasting, which is an important component of economic forecasting and inflation management. From an academic perspective, this study demonstrates the applicability of deep learning and machine learning models in economics and finance. In practice, it provides a valuable tool for economic and financial decision-makers and illuminates the way for future similar studies.

Construction Price Forecasting Models in the Construction Industry: A Comparative Analysis

Construction prices rose rapidly during 2020 and 2021, making it almost impossible for contractors to adhere to agreed contract prices. For this reason, there was a request from contractors to adjust the contract price after signing a contract for work. During the implementation of the construction contracts, they were unable to comply with the fixed contract price. Forecasting the development of price indices could solve this problem by creating a reserve that would limit the adjustment of the contract price and the contractors’ withdrawal from the contracts. The forecast could be enshrined in the contractual conditions before the start of construction, which would eliminate the risk of changing the agreed contract price for the investor and the possible occurrence of additional work. Data from statistical offices were used to create the price index forecast. In this article, four methods were used in the search for a more accurate forecast: regression analysis, exponential smoothing, the naïve method, and the Autoregressive Integrated Moving Average (ARIMA) model. From these methods, the most appropriate method was selected by multi-criteria decision-making, which was subsequently verified with actual published price index data. The main goals of this study are to determine the most suitable prognostic method for forecasting the development of the prices of construction materials and work and then comparing the forecasted data with the actual published data of statistical offices in the countries of Central Europe.

Application of The Double Exponential Smoothing Brown Method to Consumer Price Index Forecasting in Sorong City before and during The Covid-19 Pandemic

Application of The Double Exponential Smoothing Brown Method to Consumer Price Index Forecasting in Sorong City before and during The Covid-19 Pandemic

Cuban Consumer Price Index Forecasting Trough Transformer with Attention

Cuban Consumer Price Index Forecasting Trough Transformer with Attention

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

Price forecasts of ten steel products using Gaussian process regressions

Addressing price forecasting problems is an important exercise to policymakers and market participants in the resource business sector. In this work, we build Gaussian process regression models through cross validation and Bayesian optimizations over different kernels and basis functions for daily price index forecasting of ten major steel products in the Chinese market during July 20, 2011–April 15, 2021. This study aims to be the first attempt of exploring potential of Gaussian process regressions for price forecasting exercises with the coverage of all of the ten most important steel products that carry enormous economic significance in China as the largest steel consumer and producer in the world. The models offer accurate out-of-sample forecasts for the two-year period of April 16, 2019–April 15, 2021 with relative root mean square errors ranging from 0.07404% to 0.22379% across the ten price indices and correlation coefficients above 99.9%. They also lead to better forecast performance than some traditional econometric models and some other machine learning models as benchmarks. The models constructed here could be utilized by policymakers as part of policy design and implementations and by market participants as part of market assessments and decision making.

Time Time Series Forecasting of Stock Price for Maritime Shipping Company in COVID-19 Period Using Multi-Step Long Short-Term Memory (LSTM) Networks

Abstract Sea transportation is one of the most critical components of the global economy, where it is the main means of transporting huge quantities of goods across the world. The coronavirus disease (COVID-19) has caused a health-related economic crisis at the global level with widespread repercussions on sea transportation and trade. Several algorithms in machine learning and/or deep learning have been suggested as a simplification of this hard problem of stock price index forecasting, which has been the focus of much study for many years. In this study, I will propose a forecasting method based on Multi-Step Long Short-Term Memory (MS LSTM) networks to predict stock prices for three of the most important companies in the world in maritime transport. A novel optimization method for stock price prediction is proposed. It is based on a Multi-Step Long Short-Term Memory (MS LSTM) model and utilizes the Adam optimizer. Conclusions showed that using the MS LSTM algorithm, 95.44% prediction accuracy on the training data set and 95.11% prediction accuracy on the testing data set were both obtained. For the training set and testing set, respectively, it was observed that the mean absolute percentage error is 4.56% and 4.89%. By running the predictions for five days in the future, it is observed that the model has predicted that the prices would maintain their balance with little downward movement, leading to positive impact on sea shipping with lower prices expected.

Indonesian Consumer Price Index Forecasting Using Autoregressive Integrated Moving Average

The Consumer Price Index is one of the indicators used to confirm financial success in inflation management. This study aims to help determine the CPI prediction value in Indonesia for the next twelve periods in a month using the ARIMA (Autoregressive Integrated Moving Average) method using the data from January 2015 to March 2022. The results obtained show that the best model that can be used for forecasting is the ARIMA model (2,1,2) with drift with Akaike's Information Criterion (AIC) values of 2190.84. The results of Indonesia's accurate CPI forecasting can be used to assess inflation management for policymaking in the context of controlling inflation.It can be concluded that Based on the analysis, the optimal ARIMA model for forecasting Indonesia's CPI is ARIMA (2,1,2) with drift, aiding in evaluating inflation management for policymaking

Retail Property Price Index Forecasting through Neural Networks

Rapid growth has been seen in the Chinese housing market during the past decade, and housing price forecasts have become more significant to investors and policy makers. In this study, we explore neural networks for retail property price index forecasts from ten major Chinese cities for July 2005–April 2021. Our goal is to build simple and accurate neural networks to contribute to purely technical forecasts of the Chinese retail property market. To facilitate the analysis, we examine different model settings based on algorithms, delays, hidden neurons, and data splitting ratios and construct a simple neural network with five delays and two hidden neurons. This leads to a rather stable performance of about 1.6% average relative root mean square error across the ten cities for the training, validation, and testing phases. Results here can be used on a stand-alone basis or combined with fundamental forecasts to form perspectives of retail property price trends and conduct policy analyses.

IMPLEMENTATION OF THE DOUBLE EXPONENTIAL SMOOTHING (DES) METHOD TO FORECET THE CONSUMER PRICE INDEX (CPI) IN MEDAN CITY

<span lang="EN-US">The Central Statistics Agency (BPS) as a non-ministerial government agency has the task of providing data needs for the government and the public obtained through censuses and surveys conducted by BPS itself and also from other government departments or agencies as secondary data. The problem that occurs at BPS is the field of inflation, one of which is in the field of the consumer price index, which experiences price fluctuations which have an impact on the economy. Forecasting is an important tool used for effective and efficient planning, therefore, forecasting is needed to predict various events that will occur in the future. One of the methods used for forecasting is the linear Double Exponential Smoothing (DES) method from Brown, this method basically uses past data which is smoothed by exponen- tially weighting the older observation values or newer values and the data used shows trend pattern. Trend is a smoothed estimate of the average growth at the end of the period. In this study, the best parameter </span><span lang="EN-US">α</span><span lang="EN-US"> for forecasting the consumer price index in the city of Medan is </span><span lang="EN-US">α</span><span lang="EN-US">= 0,8 with a MAPE percentage of 0,0223. And the results of the consumer price index forecast in the city of Medan in 2022-2023 show an increase every month.</span>

Selection of a forecasting model for Russia consumer price index based on the application of Analytic Hierarchy Process

The present article is dedicated to the problem of making decisions in the process of selecting a forecasting model through the example of Russia Consumer Price Index (CPI) forecast. Forecast values of CPI have been obtained for a year of 2021 using five models. Qualitative criteria for the comparability of the models have been formulated in order to select the most appropriate model. The application of the Analytic Hierarchy Process gives the opportunity to determine the optimal model for the Consumer Price Index forecast based on the synthesis of quantitative preference vectors.

Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition

Accurate and reliable multi-step-ahead forecasting of stock price indexes over long-term future trends is challenging for capital investors and decision-makers. This study developed a hybrid stock price index forecasting modelling framework using Long Short-Term Memory (LSTM) with Multivariate Empirical Mode Decomposition (MEMD), which can capture the inherent features of the complex dynamics of stock price index time series. In conjunction with time–frequency analysis and deep learning algorithms, the proposed modelling framework implemented multi-step-ahead forecasting for stock price indexes using a multiple-input multiple-output (MIMO) strategy, where MEMD was first employed to simultaneously decompose the relevant features of the stock price index. Then LSTM was used to train the forecasting model by using the components extracted by MEMD and performing multi-step-ahead forecasting of the closing price of the stock price index. The hyperparameters of the LSTM model were optimized using an orthogonal array tuning method (OATM) based on the Taguchi design of experiments for enhancing the performance of prediction. Three real-world datasets were used for model validation from three exchange markets including Standard & Poor 500 index (SPX), Shanghai Stock Exchange (SSE), and Hang Seng Index (HSI). The results of the experiments suggested that the proposed hybrid model outperforms the benchmark models and improves the accuracy of multi-step-ahead forecasting.

Research on Shanghai Stock Exchange 50 Index Forecast Based on Deep Learning

After decades of advance development, China's stock market has gradually arisen into one of the world's most important capital markets. The stock price index can well reflect the health status and macro change trend of a country's economic development, which can be said to be a barometer of the country's economic development. Studying the stock price index forecast is of great significance to the entire national economy and to each investor. Using 2 tools, Python and EViews8.0, and taking the Shanghai Stock Exchange 50 index as an example, the long short-term memory (LSTM) model in deep learning (DL) and the Autoregressive Integrated Moving Average (ARIMA) model are selected for fitting and prediction. The research results explain that the Root Mean Squared Error (RMSE) of LSTM model is lower, and the model based on DL method has stronger prediction ability on stock price index than traditional stock prediction model. This model is an effective stock prediction method.

A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network

Changes in the composite stock price index are a barometer of social and economic development. To improve the accuracy of stock price index prediction, this paper introduces a new hybrid model, VMD-LSTM, that combines variational mode decomposition (VMD) and a long short-term memory (LSTM) network. The proposed model is based on decomposition-and-ensemble framework. VMD is a data-processing technique through which the original complex series can be decomposed into a limited number of subseries with relatively simple modes of fluctuations. It can effectively overcome the shortcomings of mode mixing that sometimes exist in the empirical mode decomposition (EMD) method. LSTM is an improved version of recurrent neural networks (RNNs) that introduces a “gate” mechanism, and can effectively filter out the critical previous information, making it suitable for the financial time series forecasting. The capability of VMD-LSTM in stock price index forecasting is verified comprehensively by comparing with some single models and the EMD-based and other VMD-based hybrid models. Evaluated by level and directional prediction criteria, as well as a newly introduced statistic called the complexity-invariant distance (CID), the VMD-LSTM model shows an outstanding performance in stock price index forecasting. The hybrid models perform significantly better than the single models, and the forecasting accuracy of the VMD-based models is generally higher than that of the EMD-based models.

A hybrid model combining variational mode decomposition and an attention-GRU network for stock price index forecasting.

In this paper we introduce a new hybrid model based on variational mode decomposition (VMD) and Gated Recurrent Units (GRU) network improved by attention mechanism to enhance the accuracy of stock price indices forecasting. In the process of establishing the model, VMD is made a use to decompose the primary series into some almost orthogonal subsequences. The attention mechanism is introduced into GRU to assign different weights to the input elements in advance so that better predictive results can be achieved for each component. In empirical experiment, London FTSE Index (FTSE) and Nasdaq Index (IXIC) are adopted to examine the performance of VMD-AttGRU model. Empirical results report that the developed hybrid model outperforms the single models and indeed raises the accuracy of stock price indices forecasting. In addition, the introduction of attention mechanism can increase the level predictive accuracy but decrease the correctness of direction forecasting.

Short‐term forecast of pig price index on an agricultural internet platform

AbstractFrom the perspective of agribusiness, the market price of live pigs reflects the current demand. Therefore, tracking and forecasting market prices are important tasks in agrimanagement, by which the production schedule can be adjusted to increase profit. An agricultural internet platform was developed as an integrated cloud service for market tracking. To quantitatively forecast online pig trading, in this study, a short‐term forecasting model of the pig price index was developed; the model automatically retrieved historical data as a training data set and determined the price index forecast with an autoregressive integrated moving average (ARIMA) algorithm for a time‐series analysis. The mean square error (MSE) of the AR(1) model for predicting the pig price index in Henan Province was 159.010, and the MSE of the ARIMA(1,1) model for predicting pig price index in Fujian Province was 92.294. The results demonstrated that the error between the predicted calculation and verification test results was small, and the results efficiently improved the prediction accuracy (EconLit citations: C6, L86, Q1).

Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

Tracking and Modelling Prices Using Web-Scraped Price Microdata: Towards Automated Daily Consumer Price Index Forecasting

SummaryWith the increasing relevance and availability of on-line prices that we see today, it is natural to ask whether the prediction of the consumer price index (CPI), or related statistics, may usefully be computed more frequently than existing monthly schedules allow for. The simple answer is ‘yes’, but there are challenges to be overcome first. A key challenge, addressed by our work, is that web-scraped price data are extremely messy and it is not obvious, a priori, how to reconcile them with standard CPI statistics. Our research focuses on average prices and disaggregated CPI at the level of product categories (lager, potatoes, etc.) and develops a new model that describes the joint time evolution of latent daily log-inflation rates driving prices seen on the Internet and prices recorded in official surveys, with the model adapting to various product categories. Our model reveals the differing levels of dynamic behaviour across product category and, correspondingly, differing levels of predictability. Our methodology enables good prediction of product-category-specific CPI immediately before their release. In due course, with increasingly complete web-scraped data, combined with the best survey data, the prospect of more frequent intermonth aggregated CPI prediction is an achievable goal.

Multi-model Combination Housing Price Forecasting Based on Web Search Data

In the background of large data, taking the housing price in Xi'an as an example, this paper extracts the relevant search words data from Baidu Index and builds the neural network, support vector machine and random forests model, according to the purchase information inquired by the consumers in Baidu. The gradient boosting decision tree model is established by forecast results of the three single model to achieve a combination of housing prices forecast to determine the final residential sales price index forecast. The result shows that the fitting degree of the combined model is 0.995, and the prediction accuracy is 10.4% higher than the optimal single forecasting model. This method can calculate the new commodity housing price index two weeks in advance than the National Bureau of Statistics. In future studies, this approach will be applied to many types of market forecasts to help companies and consumers make the best decisions.

Improved v -Support vector regression model based on variable selection and brain storm optimization for stock price forecasting

Big data mining, analysis and forecasting always play a vital role in modern economic and industrial fields, and selecting an optimization model to improve time series’ forecasting accuracy is challenging. A support vector regression (SVR) model is widely used forecasting and data processing, but the individual SVR cannot always satisfy the requirements of time series forecasting. In this paper, a hybrid v-SVR model is developed and combined with principal component analysis (PCA) and brain storm optimization (BSO) for stock price index forecasting. Correlation analysis and PCA are conducted initially to select the input variables of the v-SVR from 20 technical indicators, while the advanced BSO algorithm is used to search for optimal parameters of v-SVR. Case studies of the China Securities Index 300 (CSI300) and the Shenzhen Stock Exchange Component Index (SZSE Component Index) are examined as illustrative examples to evaluate the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results indicate that the developed hybrid model is not only simple but also able to satisfactorily approximate the actual CSI300stock price index, and it can be an effective tool in stock market mining and analysis.