Standard and Hybrid Deep Learning Multivariate-Multi-Step & Univariate-Multi-Step Time Series Forecasting.

[ICML 2025] Official implementation of "AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting"

An overview of univariate time series forecasting models with sample code.

this project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.

Univariate and multivariate time series forecasting, with uncertainty quantification (Python & R)

Python library to forecast univariate time series through backtesting model selection

Sensor data of a renowned power plant has given by a reliable source to forecast some feature. Initially the work has done with KNIME software. Now the goal is to do the prediction/forecasting with machine learning. The idea is to check the result of forecast with univariate and multivariate time series data. Regression method, Statistical method.

TensorFlow implementation of N-BEATS model for univariate time series forecasting.

:chart_with_upwards_trend: high level wrapper for parallel univariate time series forecasting :chart_with_downwards_trend:

Multistep univariate time series forecasting using Gated Recurrent Unit.

In this project I developed Convolutional Neural Network models for univariate , multivariate , multi-step time series forecasting.

Univariate and multivariate time series forecasting, with uncertainty quantification (Python & R)

Univariate Time Series Modeling (ARMA, ARIMA, ARFIMA), Volatility Modeling and Forecasting (Rolling Window), Value at Risk (VaR) Forecasting and Backtesting

A general framework for univariate time series forecasting.

Time Series Forecasting LSTM based on Pytorch developed with Streamlit. Supports Univariate, Multivariate and Multi-Step Forecasting.

Univariate time series forecasting using classical methods and deep learning approaches have been performed.

In this example, we will try to adopt a pure data-based approach to forecast Canada’s natural gas production using Facebook’s Prophet library. The idea of this post is to use a univariate time-series dataset and produce a best-fit model that allows us to confidently predict future production. We will be using the model to forecast the production for the next 10 years i.e. until 2030.

Demonstration of Univariate Time Series Forecasting (Long Short-Term Memory (LSTM) Network ) -- Preprocessing (Missing Values/Data Cleaning) -- Keras Time Series Generator

Empirical analysis with financial data (MSFT stock returns) in R, with the goal to produce useful forecasts using univariate, multivariate time series models and volatility models.

using various time series forecasting techniques like simple moving averages,weighted moving average,ARIMA,Univariate time-series forecasting:LSTM'S and Multi-variate & single-step forecasting:LSTM'S used to predict future stock price

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A fast, effective and accurate algorithm for univariate time series forecasting

TIMECOP is a RESTful webservice engine that evaluates univariate and multivariate timeseries. It considerates that the time series has 3 stages: the current state of the time series as the last five points, the past state as all the previous points before the current state and the future state as the forecast of the next steps(custom number).

Univariate Time Series Forecasting by LSTM

Autoregressive Convolutional RNN for univariate and multivariate time series forecasting implemented with keras and tensorflow.

The objective of the research behind the paper was to validate different methods and approaches related to sales time series data preparation, analysis and forecasting, with aim to facilitate recommending sales and marketing strategies based on trend/seasonality effects and forecasting sales of eight different groups of pharmaceutical products with diverse characteristics, such as stationarity, seasonality, amount of residuals and sales data variance. All these analyses and forecasts are made on a small scale, for a single distributor, pharmacy chain or even individual pharmacy. Paper presents only research work related to univariate time series analysis, while potential candidates for explanatory input variables were also identified and shortly elaborated. Effectiveness of three forecasting methods, namely ARIMA, Facebook’s Prophet and Long-Short Term Memory (LSTM) neural networks was investigated. Each of the method is complemented with two optimization and validation approaches, relevant for short-term (so called rolling forecast scenario) and long-term forecasting.

# Forecasting Stock Market Prices It is a **Time Series** dataset.A time series is simply a series of data points ordered in time.In a time series, time is often the independent variable and the goal is usually to make a forecast for the future. ## PROBLEM STATEMENT: Our Aim is to create a model that can forecast the future stock price based on the model training and provided dataset. ### Data We will be using a [Huge stock market dataset](https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs) from the Kaggle platform which has a very good collection of datasets.The file we will be using is present in following directory in the dataset zip file input\Data\Stocks\gs.us.txt The data is presented in CSV format as follows : Date, Open, High, Low, Close, Volume, OpenInt. Features: - Date - Open - High - Low - Close - Volume - OpenInt Note that prices have been adjusted for dividends and splits. ### LICENSE OF DATASET : [LICENSE](https://creativecommons.org/publicdomain/zero/1.0/) ### Requirements You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html) If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. This project requires **Python** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org/) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) - [statsmodels](https://www.statsmodels.org/stable/) ### Run In a terminal or command window, navigate to the top-level project directory `STOCK MARKET FORECASTING/` (that contains this README) and run one of the following commands: ipython notebook Forecasting_Stock_Market_Prices_task.ipynb or jupyter notebook Forecasting_Stock_Market_Prices_task.ipynb This will open the Jupyter Notebook software and project file in your browser. ### Steps : 1. Importing Libraries 2. Exploring the Dataset 3. Exploratory Data Analysis > * Univariate Analysis 4. Data Preprocessing 5. Model Building > * AUTOREGRESSIVE MODEL > * MOVING AVERAGE MODEL 6. Evaluation > * MEAN SQUARE ERROR > * MEAN ABSOLUTE ERROR > * ROOT MEAN SQUARE ERROR 7. Conclusion

Univariate time series forecasting

Stock Portfolio Optimization with Time Series Forecasting and Market Scenario Generation. This repository focuses on portfolio allocation with support of univariate time series forecasting and generative models for market scenario generation.

Framework for automated forecasting of univariate time series data