Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies

Abstract

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.

Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

“…Sahoo et al [19] also demonstrated the superiority of LSTM over RNN on univariate daily discharge data from the Basantapur gauging station in India's Mahanadi River basin. Suradhaniwar et al [7] demonstrated that SARIMA and SVR models outperform NN, LSTM, and RNN models when hourly averaged univariate time series data is used. Even though the best model generalization is complex, case-based analysis is the most effective method for determining which model best fits a given situation [60].…”

“…Univariate forecasting models are straightforward to train using sparse data and provide ease of inference when evaluating forecast performance. Due to the complexity of agrometeorological data, it is simpler and more efficient to forecast the variables individually [7]. On the other hand, multivariate models are designed with multiple variables such as precipitation, temperature, evaporation, and other variables as input and a streamflow variable as output [6].…”

“…However, because of the nonlinearity present in streamflow time series, the forecasting performance of these models is usually debatable [3,10]. Under one-step and multistep-ahead forecast scenarios, Suradhaniwar et al [7] compared the performance of Machine Learning (ML) and Deep Learning (DL) based time series forecasting algorithms. ey also evaluated recursive one-step forward forecasts using walk-forward validation.…”

Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

“…It also differs from classical approaches based on sliding windows and thresholds of statistical moments [3]. [1,4,16,32], [1,2,4,8,16,32], [1,3,6,12,24], [1,2,6,12,24], [1,2,4,8,16], [1,4,16], [1,2,4,8], [1,4,8] Blocks 1, 2 Dropout 0…”

“…Time series are explored in several types of applications and but not the subject of study until today. Most of these applications are focused on forecasting [32,33] and feature extraction [5] approaches, as can be seen in the following studies.…”