Dataset Information

DPP: Deep predictor for price movement from candlestick charts.

ABSTRACT: Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

SUBMITTER: Hung CC

PROVIDER: S-EPMC8216512 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

DPP: Deep predictor for price movement from candlestick charts.

Hung Chih-Chieh CC Chen Ying-Ju YJ

PloS one 20210621 6

Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick ...[more]

PMID: 34153042

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Project description:BackgroundRecurrent mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilization is an important clinical goal. This study investigates the ability of control chart methodology to predict manic and/or depressive episodes by applying Shewhart's control rules to weekly self-reported scores from mania and depression questionnaires.MethodsShewhart's control rules were applied to weekly self-reported scores from the Altman Self-Rating Mania Scale (ASRM) and the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS) collected from 2001 to 2012 as part of the OXTEXT programme. Manic and depressive episodes were defined as an ASRM score ≥ 10 or a QIDS score ≥ 15, respectively. An episode-free run-in period of eight consecutive weeks without an episode of either type was used to calibrate control charts. Shewhart's rules were then applied to follow-up data. Their sensitivity and positive predictive value for predicting manic or depressive episodes within the next 4 weeks were calculated focusing on the first episode. Secondary analyses varying control chart type, length of episode-free run-in period, time frames to evaluate diagnostic accuracy, thresholds defining either manic or depressive episodes, and missing data methods were performed.ResultsData from 146 participants (37% men) were included. The mean age was 43.4 (SD = 13.3) years. The median follow-up was 10 (IQR 5-40) weeks for mania and 10 (IQR 5-23) weeks for depression. A total of 53 (36%) participants had a manic episode and 67 (46%) had a depressive episode. For manic episodes, the sensitivity and positive predictive value of Shewhart's control rules were 30% (95% CI 19-45%) and 7% (95% CI 5-9%), and for depressive episodes, 33% (95% CI 22-46%) and 9% (95% CI 6-12%), respectively. Results from secondary analyses were similar to these.ConclusionsTele-monitoring with control rules has the potential to predict about one-third of manic or depressive episodes before they occur, at the cost of a high false positive rate. Given the severe consequences of manic and depressive episodes, this trade-off may be desirable.

Project description:BACKGROUND: Fetal movement counting has long been suggested as a screening tool to identify impaired placental function. However, quantitative limits for decreased fetal movement perform poorly for screening purposes, indicating the need for methodological refinement. We aimed to identify the main individual temporal patterns in fetal movement counting charts, and explore their associations with pregnancy characteristics. METHODS: In a population-based prospective cohort in Norway, 2009-2011, women with singleton pregnancies counted fetal movements daily from pregnancy week 24 until delivery using a modified "count-to-ten" procedure. To account for intra-woman correlation of observations, we used functional data analysis and corresponding functional principal component analysis to identify the main individual temporal patterns in fetal movement count data. The temporal patterns are described by continuous functional principal component (FPC) curves, with an individual score on each FPC for each woman. These scores were later used as outcome variables in multivariable linear regression analyses, with pregnancy characteristics as explanatory variables. RESULTS: Fetal movement charts from 1086 pregnancies were included. Three FPC curves explained almost 99% of the variation in the temporal data, with the first FPC, representing the individual overall counting time, accounting for 91% alone. There were several statistically significant associations between the FPCs and various pregnancy characteristics. However, the effects were small and of limited clinical value. CONCLUSIONS: This statistical approach for analyzing fetal movement counting data successfully captured clinically meaningful individual temporal patterns and how these patterns vary between women. Maternal body mass index, gestational age and placental site explained little of the variation in the temporal fetal movement counting patterns. Thus, a perceived decrease in fetal movement should not be attributed to a woman's basic pregnancy characteristics, but assessed as a potential marker of risk.

Project description:ObjectivesTreatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion.MethodsAll patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy.ResultsIn total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71.ConclusionsOur model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention).Key points• DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).

Dataset Information

DPP: Deep predictor for price movement from candlestick charts.

Publications

DPP: Deep predictor for price movement from candlestick charts.

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