A deep learning framework for financial time series using stacked autoencoders and long-short term memory

The process of creating a long short-term memory neural network for high-frequency financial time series analyzing and forecasting is considered in the article. The research base is compiled in the beginning. Further the estimation of long short-term memory neural network parameters is carried out on the learning subsamples. The forecast of future returns signs is made for the horizon of 90 minutes with the estimated neural network. In conclusion the trading strategy is formulated.

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Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

10.1115/ipack2021-74066 ◽

2021 ◽

Author(s):

Pradeep Lall ◽

Tony Thomas ◽

Ken Blecker

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Remaining Useful Life ◽

Operating Conditions ◽

Short Term ◽

Term Memory ◽

Rectangular Pattern ◽

Useful Life ◽

Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

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Deep Learning with Long Short-Term Memory for Time Series Prediction

IEEE Communications Magazine ◽

10.1109/mcom.2019.1800155 ◽

2019 ◽

Vol 57 (6) ◽

pp. 114-119 ◽

Cited By ~ 43

Author(s):

Yuxiu Hua ◽

Zhifeng Zhao ◽

Rongpeng Li ◽

Xianfu Chen ◽

Zhiming Liu ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Sensors ◽

10.3390/s20174887 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4887

Author(s):

Hailun Zhang ◽

Rui Fu

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Hybrid Approach ◽

Lateral Acceleration ◽

Short Term ◽

Lateral Velocity ◽

Term Memory ◽

Turning Behavior ◽

Long Short Term Memory

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory

International Journal of Machine Learning and Computing ◽

10.18178/ijmlc.2021.11.1.1015 ◽

2021 ◽

Vol 11 (1) ◽

pp. 61-67

Author(s):

Watthana Pongsena ◽

◽

Prakaidoy Sitsayabut ◽

Nittaya Kerdprasop ◽

Kittisak Kerdprasop ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Price Changes ◽

Term Memory ◽

The Future ◽

Long Short Term Memory

Forex is the largest global financial market in the world. Traditionally, fundamental and technical analysis are strategies that the Forex traders often used. Nowadays, advanced computational technology, Artificial Intelligence (AI) has played a significant role in the financial domain. Various applications based on AI technologies particularly machine learning and deep learning have been constantly developed. As the historical data of the Forex are time-series data where the values from the past affect the values that will appear in the future. Several existing works from other domains of applications have proved that the Long-Short Term Memory (LSTM), which is a particular kind of deep learning that can be applied to modeling time series, provides better performance than traditional machine learning algorithms. In this paper, we aim to develop a powerful predictive model targeting to predicts the daily price changes of the currency pairwise in the Forex market using LSTM. Besides, we also conduct an extensive experiment with the intention to demonstrate the effect of various factors contributing to the performance of the model. The experimental results show that the optimized LSTM model accurately predicts the direction of the future price up to 61.25 percent.

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Forecasting container throughput with long short-term memory networks

Industrial Management & Data Systems ◽

10.1108/imds-07-2019-0370 ◽

2019 ◽

Vol 120 (3) ◽

pp. 425-441 ◽

Cited By ~ 4

Author(s):

Sonali Shankar ◽

P. Vigneswara Ilavarasan ◽

Sushil Punia ◽

Surya Prakash Singh

Keyword(s):

Time Series ◽

Deep Learning ◽

Short Term Memory ◽

Moving Average ◽

Short Term ◽

Error Matrix ◽

Term Memory ◽

Content Type ◽

Forecasting Performance ◽

Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

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Comparison of Long Short-Term Memory Networks and Random Forest for Sentinel-1 Time Series Based Large Scale Crop Classification

Remote Sensing ◽

10.3390/rs13245000 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5000

Author(s):

Felix Reuß ◽

Isabella Greimeister-Pfeil ◽

Mariette Vreugdenhil ◽

Wolfgang Wagner

Keyword(s):

Time Series ◽

Deep Learning ◽

Large Scale ◽

Short Term Memory ◽

Meteorological Data ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Crop Classification

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications.

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Land subsidence modelling using a long short-term memory algorithm based on time-series datasets

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-382-505-2020 ◽

2020 ◽

Vol 382 ◽

pp. 505-510

Author(s):

Huijun Li ◽

Lin Zhu ◽

Huili Gong ◽

Hanrui Sun ◽

Jie Yu

Keyword(s):

Time Series ◽

Deep Learning ◽

Land Subsidence ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Data Volume ◽

Artificial Intelligence Technology ◽

Long Short Term Memory ◽

Input Variables

Abstract. With the rapid growth of data volume and the development of artificial intelligence technology, deep-learning methods are a new way to model land subsidence. We utilized a long short-term memory (LSTM) model, a deep-learning-based time-series processing method to model the land subsidence under multiple influencing factors. Land subsidence has non-linear and time dependency characteristics, which the LSTM model takes into account. This paper modelled the time variation in land subsidence for 38 months from 2011 to 2015. The input variables included the change in land subsidence detected by InSAR technology, the change in confined groundwater level, the thickness of the compressible layer and the permeability coefficient. The results show that the LSTM model performed well in areas where the subsidence is slight but poorly in places with severe subsidence.

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