Forecasting Consumer Price Index (Cpi) Using Time Series Models and Multi Regression Models (Albania Case Study)

Dhamo, Eralda; Puka, Llukan; Zaçaj, Oriana

doi:10.3846/bm.2018.51

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Stationarity refers to the statistical properties of a process generating a time series not changing over time. In [9] and [10], the Albanian CPI was forecasted based on multiple, regression models, Seasonal Autoregressive Integrated Moving Average (SARIMA), and ETS, with the SARIMA model shown to be more than accurate than the ETS model. The SARIMA time series model was also shown to be a better CPI predictor than multiple regression models.…”

Section: Prior Related Workmentioning

confidence: 99%

Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods

N. Sibai,

El-Moursy,

Sibai

2024

IJCDS

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The Consumer Price Index (CPI) is an indicator of inflation and is tracked by many government and economic agencies to make decisions of major importance. Its prediction is a valuable input into government policies such as taxation, and it greatly impacts the cost of borrowing money. The CPI has been traditionally predicted with statistical methods such as the Autoregressive Integrated Moving Average (ARIMA) model. In this paper, we forecast the Saudi Arabian Consumer Price Index with six machine learning (ML) methods, using the Orange 3 data mining and analytics tool, and based on the published historical January 2013 to November 2020 CPI data. We compare the performances of Decision Tree (Tree), k-Nearest Neighbors (kNN), Linear Regression (LR), Neural Networks (NN), Random Forest (RF), and Support Vector Machine (SVM), all applied to the 2013-2020 Saudi Arabian CPI dataset. Multiple experiments were conducted to vary the training and testing sets, optimize the machine learning parameters, and improve the MSE and R 2 metrics. The predicted CPI values of these ML methods were also compared to the 2021-2024 International Monetary Fund (IMF) CPI forecast and the actual 2021-2024 CPIs (post mortem). The results indicate that the multilayer perceptron neural network model outperforms the other ML models, is nearest to the actual CPI, and may be used to forecast the CPI for up to 3 years from the latest CPI data in the training dataset. The kNN model follows the neural network model in second place. The best fitting Excel trend line underperformed all ML methods in forecasting the Saudi Arabian CPI.

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Section: Prior Related Workmentioning

confidence: 99%

Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods

N. Sibai,

El-Moursy,

Sibai

2024

IJCDS

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“…Gjika et al [11] studied CPI in Albania using time series and multiregression technique. The time series models was used to forecast CPI while the multiregression was used to simulate forecast for macroeconomic sub component with significant correlation with CPI.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecasting of Consumer Price Index (CPI) in Kenya and South Africa using Holt Winter Model

Njenga

2024

AJEBA

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In this paper, Holt-Winters exponential smoothing approach is applied to model and forecast monthly CPI in Kenya and South Africa. Monthly data from January 2000 to December 2023 was obtained from Central Bank of Kenya and South Africa department of statistics. Time series decomposition showed that the trend component is the most dominant component in both countries. Kenya Holt-Winters estimated model has parameters 0.6756, 0.0077 and 1 for level smoothing, trend smoothing and seasonal smoothing respectively. On the other hand, South Africa estimated model has parameters 0.8917, 0.1057 and 1 for level smoothing, trend smoothing and seasonal smoothing respectively. The estimated models are efficient and effective as on average the fitted values are less than one percent off the observed values. The initial values for level smoothing, trend smoothing and seasonal smoothing are approximately equal in both countries. The estimated models are then used to predict CPI next twelve months. Over the forecast period, South Africa will experience a lower index as compared to Kenya. In both countries, it’s expected that monthly CPI will rise.

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“…They found a significant relationship between these indices. Gjika et al (2018) in their study have analyzed and forecasted economic indices for Albania using an econometric model (data for period . They conclude that the exchange rate and the number of people traveling abroad were positively correlated to CPI.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Albanian economic development using multivariate Markov chain model

Gjika

Basha

Allka

et al. 2020

11th International Scientific Conference “Business and Management 2020”

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In this work, the economic development and relation to social and demography indices in Albania were studied. Four time series (yearly data for the period 1995–2018) were considered: consumer price index (CPI), unemployment rate, inflation and life expectancy. In our approach, a first and fifth order multivariate Markov chain model was proposed to predict the economic situation in Albania in the proceedings years. Tests and accuracy analysis of the model were performed. The prediction probabilities fall in the interval of 0.47 to 0.52 and the accuracy of both models is 75%. Our approach is a short term probability forecast model that can be used by the policymakers to evaluate and undertake initiatives to improve the situation in the country.

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Forecasting Consumer Price Index (Cpi) Using Time Series Models and Multi Regression Models (Albania Case Study)

Cited by 9 publications

References 13 publications

Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods

Forecasting The Consumer Price Index: A Comparative Study of Machine Learning Methods

Analysis and Forecasting of Consumer Price Index (CPI) in Kenya and South Africa using Holt Winter Model

Predicting the Albanian economic development using multivariate Markov chain model

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