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Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies
- Source :
- Sensors, Vol 21, Iss 2430, p 2430 (2021), Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 7
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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.
- Subjects :
- 0106 biological sciences
Computer science
02 engineering and technology
computer.software_genre
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
support vector machines
Analytical Chemistry
internet-of-things (IoT)
Moving average
0202 electrical engineering, electronic engineering, information engineering
multi-step ahead forecasting
multilayer perceptron
recurrent neural networks
time series forecasting
lcsh:TP1-1185
Autoregressive integrated moving average
Electrical and Electronic Engineering
Time series
Instrumentation
seasonal auto-regressive models
precision agriculture
Artificial neural network
Model selection
long-short-term-memory
Univariate
Atomic and Molecular Physics, and Optics
walk-forward validation
Autoregressive model
Multilayer perceptron
temporal bifurcation
020201 artificial intelligence & image processing
Data mining
computer
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 2430
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....7500aa270fcffedf438392b09ec4d1e7