TY - BOOK AU - Akshaya Ajith AU - Sajitha Vijayan, M(Guide) TI - Comparative analysis of price forecasting models for black pepper U1 - 630.31 PY - 2024/// CY - Vellanikkara PB - Department of Agricultural Statistics, College of Agriculture KW - Agricultural Statistics KW - Black pepper KW - Forecasting KW - Artificial neural network N1 - MSc N2 - ABSTRACT Black pepper, the ‘king of spices’ is one of the most popular and widely consumed spices which shares a place on most dinner tables with salt. India is the third largest producer in the world (International Pepper Community, 2023), also a significant consumer and exporter of black pepper, with Kerala and Karnataka producing the majority of the nation's output. Kerala ranked second in terms of black pepper acreage (76,160 ha), and production (33290 metric tonnes) but seventh in terms of productivity (0.44 metric tonnes/ha) (GOI, 2023). Historically, the market value of pepper contributed to the development of the city of Kochi as a centre of international commerce. Kochi has the first exclusive pepper exchange in India which was established by the Indian Pepper and Spice Traders Association, IPSTA and the exchange was well regulated by the traditional players here, without any default on supply or delivery of the commodity and volatility. As per latest records of Spices Board, the price of black pepper surpassed Rs.500/Kg which was the maximum price in the past years since 2022 in Kochi market and this indicated that black pepper prices are highly volatile. Being a perennial crop, the large variation in prices of black pepper within a year is a major problem faced by farmers as well as consumers. Hence, analysis of time series data of prices of black pepper is of prime importance. In this context, the present study was undertaken to evaluate different time series models for prices of black pepper and to suggest suitable forecast models for Kochi market. Time series data on monthly and weekly average prices of garbled and ungarbled black pepper at Kochi market from January 2000 to December 2020, collected from Spices Board, Kochi formed the database for the study. Analysis of price pattern revealed that wide fluctuation exists in the prices of black pepper in Kochi market. In order to have a general idea about trend of prices of black pepper, models like linear, exponential and quadratic, were fitted. From among several models tried, exponential model was found to be best fit for the monthly and weekly prices of both garbled and ungarbled black pepper. To examine the time series data for the price of black pepper, a multiplicative model was employed for decomposition. Seasonal indices for the 12 months from January to December was calculated for both garbled and ungarbled black pepper prices as the seasonal variation were present in monthly and weekly data. Different traditional time series models such as exponential smoothing models (single, double, Holt-Winters’ multiplicative models (HWMS)), Auto Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Auto Regressive Conditional Heteroskedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) were applied to the price data using R software. The Augmented Dickey-Fuller test and Heteroscedasticity Lagrange’s Multiplier test were used to test the stationarity and volatility of the time-series respectively. In addition to the traditional method of price forecasting, machine learning techniques like Artificial Neural Network (ANN), and Recurrent Neural Network (RNN) were applied to the data. An artificial neural network (ANN) is a mathematical model that aims to replicate the functionality and architecture of biological neural networks. Time- delay Neural Network (TDNN) which is a major type of ANN was employed for this temporal data as it uses time delays at the input layer of the network to build a short-term memory model for forecasting the prices of black pepper. An artificial neural network with a recurrent topology is called a recurrent neural network. Long Short-Term Memory (LSTM) neural network, a specialized type of RNN, as it possesses the capability to learn patterns with long dependencies and is adept at detecting complex patterns. The price data was split into training and testing data in the ratio of 80:20. The best forecasting model was determined based on the lower values of the Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE). The predictability performance of the selected model was also evaluated using MAPE. The HWMS model was observed as the finest among the different exponential smoothing models for this time series data on prices of garbled and ungarbled black pepper. SARIMA(2,1,2)(3,0,2)12 , SARIMA(2,1,2)(2,0,2) 12, SARIMA(2,1,2) (1,0,0)52 and SARIMA(1,1,1) (1,0,1)52 were identified best among several ARIMA models tried for monthly and weekly garbled and ungarbled black pepper respectively. The GARCH (1,1) was considered best among the different ARCH family models for this price series data. TDNN (6:2s:1l), TDNN(6:3s:1l), TDNN (13:8s:1l) and TDNN (12:7s:1l) models were found to be the pinnacle artificial neural network model with lower MAPE values 4.29, 4.63, 1.99 and 2.22 in the case of monthly and weekly prices of garbled and ungarbled black pepper respectively. The results revealed that the TDNN model showed superiority in price forecasting of black pepper in all the cases when compared with all other models. Thus, the TDNN model was used to forecast the prices from January 2021 to December 2022. The MAPE value between the actual and forecasted prices for 2021 and 2022 was 4.19 and 4.86 respectively for monthly price of garbled black pepper, while for monthly price of ungarbled black pepper it was 4.09 and 5.05 respectively. The MAPE value between the actual and forecasted prices for 2021 and 2022 was 3.11 and 3.16 respectively for weekly price of garbled black pepper, while for weekly price of ungarbled black pepper it was 3.36 and 3.58 respectively. In conclusion, the analysis suggested that the TDNN model proves to be a reliable forecasting tool for predicting prices of black pepper in the Kochi market. The robustness of the TDNN model offers a plethora of opportunities for understanding the future price pattern of black pepper which enables, various stakeholders such as producers and traders to adapt with the price fluctuations and for policymakers to ensure market stability. The TDNN model's reliability in forecasting black pepper prices not only enhances market transparency fostering overall market efficiency in India ER -