Code repository for the online course "Feature Engineering for Time Series Forecasting".

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

Forecasting Time Series with Moving Average and Exponential Smoothing

Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)

Investigation of the capabilities of foundations models in the context of time series forecasting

Analysis Sales data to gain insights and create Interactive Sales Dashboard and also predict /Forecast the next sales with the use of Power-Bi.

Automatically select DNNs for time series forecasting under consideration of complexity and resource consumption.

Analyze and propose the plan to monitor and estimate business aspects

Solar Irradiance Forecasting Using Deep Learning Techniques

Dhaka weather forecasting model trained using API from Open-Meteo.com

Analyzing retail sales data to craft targeted marketing, elevate customer experiences, and forecast future sales.

Two projects developed as part of a university course on Artificial Neural networks and Deep Learning, in particular, an image classification task, and a univariate time series forecasting task.

Exploring forecasting for energy consumption

Implementing method of Willemain et al., 2004 for forecasting intermittent demand

In this section, we will use machine learning algorithms to perform time series analysis.

data and code associated with the publication "Age structure augments the predictive power of time series for fisheries and conservation" by Tara E. Dolan, Eric P. Palkovacs, Tanya L. Rogers, and Stephan B. Munch.

Repository for replicating and comparing time series LLM models with statistical models

A multivariate time series forecasting of pollution data using ARIMA, LM & ARIMAX in R

Highly accurate forecasting model for predicting Bitcoin prices in January 2024 using deep learning techniques, specifically Recurrent Neural Networks (RNNs).