Forecasting latex crepe prices in the Sri Lankan market: A comparison of conventional time series models with artificial neural networks (ANN) (Record no. 75224)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02098nam a22001577a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Wijesuriya, W. |
| 245 ## - TITLE STATEMENT | |
| Title | Forecasting latex crepe prices in the Sri Lankan market: A comparison of conventional time series models with artificial neural networks (ANN) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Proceedings of the IRRDB International Rubber Conference 2023, 20-21 February 2023, IRRDB, Kuala Lumpur, Malaysia, pp. 107-112. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This study attempted to forecast Latex Crepe Grade 1 (LC-1) prices of the Sri Lankan market through Seasonal Auto Regressive Integrated Moving Average (SARIMA), Holt Winter’s exponential smoothing (HW_ETS) and Artificial Neural Network (ANN) models. A monthly time series data of LC-1 from January 1986 to December 2020 were used for estimation of LC-1 prices with SARIMA and HW_ETS. For Artificial Neural Network (ANN) models, 70% of the data set was used for training and the rest was used for testing. To forecast LC-1 prices, three time periods viz. three, six and 12 months were used for all models beyond January 2021. The variables, international rubber prices (IntP), exchange rates, Sri Lankan rupee per US$ (SLR_$) and Japanese Yen per US$ (Yen_$) and crude oil prices (Cr_OP) were used with their first two lags as independent variables in ANN, together with 12 lags of monthly LC-1. The forecast performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The selected ANN models (ANN_M1: 12 lags of LC-1 and ANN_M2: 12 lags of LC-1 with Cr_OP and its first 2 lags) yielded relatively better forecast performances than the conventional time series models and the other ANN models tested. For 12 and six months forecasts, model M_1 performed better and for three months forecasts, the model M_2 was found to be the best model with acceptable ranges of MAPE (8-9%) and RMSE (62-63). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial Neutral Networks (ANNs) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Latex crepe prices |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Price forecasting |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Time series models |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Rathnayaka, D., Sankalpa, S. and Ishani, P.G.N. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journals |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Date acquired | Koha item type |
|---|---|---|---|---|---|---|---|
| RRII Library | RRII Library | 25/04/2024 | Journals |