{"id":16830488,"url":"https://github.com/didierrlopes/univariatetimeseriesforecast","last_synced_at":"2025-08-10T06:33:58.926Z","repository":{"id":108311276,"uuid":"236593054","full_name":"DidierRLopes/UnivariateTimeSeriesForecast","owner":"DidierRLopes","description":"PhD Thesis: \"Data Science in the Modeling and Forecasting of Financial Timeseries: from Classic methodologies to Deep Learning\"","archived":false,"fork":false,"pushed_at":"2021-07-01T21:30:24.000Z","size":19016,"stargazers_count":33,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-07-17T04:07:20.915Z","etag":null,"topics":["arima-model","deep-learning","lstm-neural-networks","phd-thesis","time-series","time-series-forecasting"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DidierRLopes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-01-27T20:56:17.000Z","updated_at":"2025-06-04T01:56:57.000Z","dependencies_parsed_at":"2023-03-14T21:30:55.311Z","dependency_job_id":null,"html_url":"https://github.com/DidierRLopes/UnivariateTimeSeriesForecast","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/DidierRLopes/UnivariateTimeSeriesForecast","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DidierRLopes%2FUnivariateTimeSeriesForecast","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DidierRLopes%2FUnivariateTimeSeriesForecast/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DidierRLopes%2FUnivariateTimeSeriesForecast/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DidierRLopes%2FUnivariateTimeSeriesForecast/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DidierRLopes","download_url":"https://codeload.github.com/DidierRLopes/UnivariateTimeSeriesForecast/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DidierRLopes%2FUnivariateTimeSeriesForecast/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269687868,"owners_count":24459386,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-10T02:00:08.965Z","response_time":71,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arima-model","deep-learning","lstm-neural-networks","phd-thesis","time-series","time-series-forecasting"],"created_at":"2024-10-13T11:39:20.185Z","updated_at":"2025-08-10T06:33:58.854Z","avatar_url":"https://github.com/DidierRLopes.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Univariate Time Series Forecast\n\nThis study was developed with Filipe Roberto Ramos (https://ciencia.iscte-iul.pt/authors/filipe-roberto-de-jesus-ramos/cv) for his phD thesis entitled \"Data Science in the Modeling and Forecasting of Financial Timeseries: from Classic methodologies to Deep Learning\". Submitted in 2021 to Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal.\n\n\n\u003c!-- TABLE OF CONTENTS --\u003e\n\n\u003col\u003e\n  \u003cli\u003e\n    \u003ca href=\"#Notebooks\"\u003eNotebooks\u003c/a\u003e\n    \u003cul\u003e\n      \u003cli\u003e\u003ca href=\"#ExploratoryDataAnalysis\"\u003eExploratoryDataAnalysis\u003c/a\u003e\u003c/li\u003e\n      \u003cli\u003e\u003ca href=\"#Arima-and-Sarima\"\u003eArimaAndSarima\u003c/a\u003e\u003c/li\u003e\n      \u003cli\u003e\u003ca href=\"#ExponenTialSmoothing\"\u003eExponenTialSmoothing\u003c/a\u003e\u003c/li\u003e\n      \u003cli\u003e\u003ca href=\"#DeepNeuralNetwork\"\u003eDeepNeuralNetwork\u003c/a\u003e\u003c/li\u003e\n    \u003c/ul\u003e\n  \u003c/li\u003e\n  \u003cli\u003e\u003ca href=\"#Citation\"\u003eCitation\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\n## Notebooks\n\n### ExploratoryDataAnalysis\n* Imports and Defines\n* Data Inspection\n* Data Visualization\n* Data Analysis\n* Hypothesis Test\n\n### ARIMA and SARIMA \n* Imports and Defines\n  * Imports\n  * Define Functions\n  * Define Univariate Time-Series\n* Stationarity of the Time-Series\n  * Data transformation and its graphical representation\n  * Normality tests\n   * Jarques-Bera\n   * Kolmogorov-Smirnov\n  * Unit Root and Stationarity Tests\n   * The Augmented Dickey-Fuller test\n   * Kwiatkowski-Phillips-Schmidt-Shin\n  * Correlation plots\n* (S)ARIMA Selection\n  * Pre-processing\n  * Model training\n  * Model Comparison based on Information Criteria\n  * Selected Models Information Criteria Comparison\n  * Selected Models Cross-Validation\n* Model Validation\n  * Model Residual Analysis\n  * Normality tests\n   * Kurtosis and Kurtosis Test\n   * Skew and Skewness Test\n   * Jarque-Bera and Kolmogorov-Smirnov tests\n  * Engle's Test for Autoregressive Conditional Heteroscedasticity (ARCH)\n  * Test for No Autocorrelation\n   * Brock–Dechert–Scheinkman test\n   * Breusch-Godfrey test [NOT IN SARIMA]\n   * Box-Pierce and Ljung-Box tests\n   * QQplot\n   * Auto-correlation and Partial Auto-correlation functions\n* Model Prediction\n  * Model Prediction Overview\n\n\n### ExponenTialSmoothing\n* Imports and Defines\n  * Imports\n  * Define Functions\n  * Define Univariate Time-Series\n* Data Visualization\n* Model Training\n  * Single Exponential Smoothing\n   * TS (N, N) - Simple Exponential Smoothing\n  * Double Exponential Smoothing\n   * TS (A, N) - Holt’s linear method\n   * TS (Ad, N) - Additive damped trend method\n  * Triple Exponential Smoothing\n   * TS (N, A) method\n   * TS (A, A) - Additive Holt-Winters method\n   * TS (Ad, A) method\n   * TS (N, M) method\n   * TS (A, M) Multiplicative Holt-Winters’ method\n   * TS (Ad, M) Holt-Winters’ damped method\n* Model Selection\n* Model Validation\n  * Normality Test\n   * Kurtosis and Kurtosis Test\n    * Skew and Skewness Test\n    * Jarque-Bera test\n    * Kolmogorov-Smirnov test\n  * Engle's Test for Autoregressive Conditional Heteroscedasticity (ARCH)\n  * Test for No Autocorrelation\n   * Brock–Dechert–Scheinkman test\n   * Box-Pierce and Ljung-Box tests\n   * QQplot\n   * Plot Auto-correlation and Partial Auto-correlation functions\n* Model Prediction\n  * Model Prediction Overview\n  \n  \n### DeepNeuralNetwork\nAnd **DNN_OurApproach**\n\n* Imports and Defines\n  * Imports\n  * Define Functions\n  * Define Univariate Time-Series\n* Training Deep Neural Network\n  * Data Pre-Processing\n  * Visualization of Data Pre-Processed\n  * Model Selection (tune hyper-parameters)\n  * Cross-Validation\n   * Compute Cross-Validation Errors\n   * Cross-Validation Performance\n   * Cross-Validation Plot\n* Model forecasting\n   * Perform statistics on predictions\n   * Statistics cleaning\n   * Plot Prediction\n   * Plot Forecast in-sample vs out-sample\n\n\n## Citation\n\nAPA \n\n`Ramos, F. (2021). Data Science na Modelação e Previsão de Séries Económico-financeiras: das Metodologias Clássicas ao Deep Learning. (PhD Thesis submitted, Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal).`\n\n```\n@phdthesis{FRRamos2021,\n      AUTHOR = {Filipe R. Ramos},\n      TITLE = {Data Science na Modelação e Previsão de Séries Económico-financeiras: das Metodologias Clássicas ao Deep Learning},\n      PUBLISHER = {PhD Thesis submitted, Instituto Universitário de Lisboa - ISCTE Business School, Lisboa, Portugal},\n      YEAR =  {2021}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdidierrlopes%2Funivariatetimeseriesforecast","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdidierrlopes%2Funivariatetimeseriesforecast","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdidierrlopes%2Funivariatetimeseriesforecast/lists"}