Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Siami‐Namini, Sima; Namin, Akbar Siami

doi:10.48550/arxiv.1803.06386

Cited by 49 publications

(39 citation statements)

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“…Similarly, another comparison between ARIMA and LSTM was discussed by Akbar Siami Namini (2018) [26], where both methods were used for the forecasting of economic and financial time series. The purpose of the research was to discuss the positions of deep learning models as compared to traditional statistical models.…”

Section: Related Workmentioning

confidence: 99%

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

Rasheed¹,

Younis²,

Ahmad³

et al. 2021

Preprint

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Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors. Future planning of commodity pricing is achievable by forecasting their future price anticipated by the current circumstances. This paper presents a wheat price forecasting methodology, which uses the price, weather, production, and consumption trends for wheat prices taken over the past few years and analyzes them with the help of advance neural networks architecture Long Short Term Memory (LSTM) networks. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods. INDEX TERMSTime-series analysis, Machine learning, Economic forecasting, Artificial neural networks VOLUME 0, 0000

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Section: Related Workmentioning

confidence: 99%

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

Rasheed¹,

Younis²,

Ahmad³

et al. 2021

Preprint

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“…Typical LSTM models are Vanilla LSTM [30], Stacked LSTM [31], Bidirectional LSTM [32], etc. Although the LSTM model generally outperforms the ARIMA model in time series prediction [33], the ARIMA model outperforms the LSTM in time series data with strong seasonal factors [34]. Studies for the interpretation of LSTMs and RNNs were published in the visual analytics community.…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models

et al. 2021

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The output of a deep-learning model delivers different predictions depending on the input of the deep learning model. In particular, the input characteristics might affect the output of a deep learning model. When predicting data that are measured with sensors in multiple locations, it is necessary to train a deep learning model with spatiotemporal characteristics of the data. Additionally, since not all of the data measured together result in increasing the accuracy of the deep learning model, we need to utilize the correlation characteristics between the data features. However, it is difficult to interpret the deep learning output, depending on the input characteristics. Therefore, it is necessary to analyze how the input characteristics affect prediction results to interpret deep learning models. In this paper, we propose a visualization system to analyze deep learning models with air pollution data. The proposed system visualizes the predictions according to the input characteristics. The input characteristics include space-time and data features, and we apply temporal prediction networks, including gated recurrent units (GRU), long short term memory (LSTM), and spatiotemporal prediction networks (convolutional LSTM) as deep learning models. We interpret the output according to the characteristics of input to show the effectiveness of the system.

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“…To capture timeseries specific properties, various categories of models were proposed such as (i) Univariate models, which can only model the endogenous variables, like MA, ARMA, ARIMA and its variants such as SARIMA (ii) Multivariate models which can model exogenous variables along with the endogenous variables like VAR models along with its variants such as elliptical [5], structured VAR model [6], ARIMAX, SARI-MAX, etc. Along with these time-series specific models, many classical machine learning models found their way into the problem of time-series forecasting such as support vector regression [7], LASSO [8], gaussian processes [9] and even recent deep learning techniques such as LSTMs [10]. Apart from using single models, various ensemble models [11]- [13] were designed and tested to improve the accuracy of timeseries forecasting problems.…”

Section: Related Workmentioning

confidence: 99%

“…The feature representation F(d) is a combination of WD, DQ, and AG based historical features. The final combined feature representation is of dimension size [10]. The LSTM model has a stack of two LSTM layers to encode the input features and the output from the last time step of the second LSTM layer is considered as an input to a Dense layer which forecasts for 30 days.…”

Section: Feature Representationmentioning

confidence: 99%

A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data

Jain¹,

Marvaniya²,

Godbole³

et al. 2020

Preprint

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Accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered every day, which can be prone to human-induced errors like the entry of incorrect data or entry of no data for many days.In addition to such human prone errors, the fluctuations in the prices itself make the creation of stable and robust forecasting solution a challenging task. Considering such complexities in crop price forecasting, in this paper, we present techniques to build robust crop price prediction models considering various features such as (i) historical price and market arrival quantity of crops, (ii) historical weather data that influence crop production and transportation, (iii) data quality-related features obtained by performing statistical analysis. We additionally propose a framework for context-based model selection and retraining considering factors such as model stability, data quality metrics, and trend analysis of crop prices. To show the efficacy of the proposed approach, we show experimental results on two crops -Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach not only improves accuracy metrics significantly when compared against the standard forecasting techniques but also provides robust models.

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Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Cited by 49 publications

References 0 publications

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models

A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data

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