Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

Sirisha, Uppala Meena; Belavagi, Manjula C.; Attigeri, Girija

doi:10.1109/access.2022.3224938

Cited by 56 publications

(17 citation statements)

References 25 publications

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“…For the selection of the best-fit model, recommendations from Kurunç, Yürekli and Çevik (2005), Valipour (2015), and Sirisha, Belavagi and Attigeri (2022) were employed to identify the most suitable model. It is evident that the values of p, d, and q were varied within the range of 0 to 10 to ascertain the optimal ARIMA models.…”

Section: Study Methodsmentioning

confidence: 99%

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

Tan Kesgin,

Demir,

Kesgin

et al. 2023

Journal of Water and Land Development

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Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels. The surface water level variability was forecast using conventional machine learning models, including autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA). Based on the monthly water levels of Beyşehir Lake from 1992 to 2016, future water levels were predicted up to 24 months in advance. Water level predictions were obtained using conventional time series stochastic models, including autoregressive moving average, autoregressive integrated moving average, and seasonal autoregressive integrated moving average. Using historical records from the same period, prediction models for precipitation and evaporation were also developed. In order to assess the model's accuracy, statistical performance metrics were applied. The results indicated that the seasonal autoregressive integrated moving average model outperformed all other models for lake level, precipitation, and evaporation prediction. The obtained results suggested the importance of incorporating the seasonality component for climate predictions in the region. The findings of this study demonstrated that simple stochastic models are effective in predicting the temporal evolution of hydrometeorological variables and fluctuations in lake water levels.

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Section: Study Methodsmentioning

confidence: 99%

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

Tan Kesgin,

Demir,

Kesgin

et al. 2023

Journal of Water and Land Development

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“…One study focused on using the ARIMA model for interrupted time series analysis to assess large-scale health intervention measures [8]. The ARIMA model has performed well in many practical applications [9]- [11].…”

Section: A Related Work In Time Series Forecastingmentioning

confidence: 99%

A Denoising Time Window Algorithm for Optimizing LSTM Prediction

Wan

2024

IEEE Access

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Realistic anomalous data noise is usually locally distributed in nature. To address the challenge posed by clustered anomalous noise in time series data, which not only reduces the accuracy of forecasting models but also presents a unique challenge due to its clustered nature and existing methods' limitations in handling such noise, this study introduces a novel denoising algorithm leveraging an optimized Long Short-Term Memory (LSTM) network, named 'Denoising Time Window Optimized LSTM' (DTW-LSTM), which innovatively identifies and rectifies clustered anomalous noise in time series data, significantly enhancing prediction accuracy by adeptly balancing the denoising process and data integrity preservation, as evidenced by comprehensive experimental validations. The algorithm employs a sliding time window mechanism to analyze the original series and its Fourier approximation, identifying clustered anomalies through a clustering model. It then uses a symmetrical data mixing and crossover technique to enhance the data quality in noisy segments. This process preserves the integrity of time series data by maintaining a balance between the original and fitted series, leading to a significant improvement in prediction accuracy. Experimental results indicate that the algorithm can adeptly discern the presence of clustered anomalous noise within the current time window and effectively rectify sections of data containing such noise. By surpassing the performance benchmarks set by contemporary forecasting models, this algorithm has established itself as the new SOTA method in the field of time series forecasting. Each sub-model contributes to the superior accuracy and reliability of the algorithm.

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“…For example, Historical Averaging (HA) takes the average of the flow value in the past period as the flow value in the next moment. Linear Prediction [9], the Auto Regression Integrated Moving Average Model (ARIMA) model [10,11], and the Seasonal Auto Regression Integrated Moving Average Model (SARIMA) [12,13] are classical forecasting methods that usually have complete theoretical support and a good performance in time series with obvious periodicity and smoothness. However, with the strengthening of end-user mobility, network traffic data also has a certain degree of regular distribution in space, the traditional linear time series methods only analyze the characteristics of traffic data on the time axis, which can no longer obtain accurate prediction results.…”

Section: Related Workmentioning

confidence: 99%

A Novel Cellular Network Traffic Prediction Algorithm Based on Graph Convolution Neural Networks and Long Short-Term Memory through Extraction of Spatial-Temporal Characteristics

et al. 2023

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In recent years, cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public’s pursuit of a better life. Accurate prediction of cellular network traffic can help operators avoid wasting resources and improve management efficiency. Traditional prediction methods can no longer perfectly cope with the highly complex spatiotemporal relationships of the current cellular networks, and prediction methods based on deep learning are constantly growing. In this paper, a spatial-temporal parallel prediction model based on graph convolution combined with long and short-term memory networks (STP-GLN) is proposed to effectively capture spatial-temporal characteristics and to obtain accurate prediction results. STP-GLN is mainly composed of a spatial module and temporal module. Among them, the spatial module designs dynamic graph data based on the principle of spatial distance and spatial correlation. It uses a graph convolutional neural network to learn the spatial characteristics of cellular network graph data. The temporal module uses three time series based on the principle of temporal proximity and temporal periodicity. It uses three long and short-term memory networks to learn the temporal characteristics of three time series of cellular network data. Finally, the results learned from the two modules are fused with different weights to obtain the final prediction results. The mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) are used as the performance evaluation metrics of the model in this paper. The experimental results show that STP-GLN can more effectively capture the spatiotemporal characteristics of cellular network data; compared with the most advanced model in the comparison model on the real cellular traffic dataset in one cell, the RMSE can be improved about 81.7%, the MAE is improved about 82.7%, and the R2 is improved about 2.2%.

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Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison

Cited by 56 publications

References 25 publications

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

A Denoising Time Window Algorithm for Optimizing LSTM Prediction

A Novel Cellular Network Traffic Prediction Algorithm Based on Graph Convolution Neural Networks and Long Short-Term Memory through Extraction of Spatial-Temporal Characteristics

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