Time Series Labs

This repository contains Jupyter notebooks used for training during time series course delivery.

Contents

1. AR Modelling

2. ARIMA

3. Multilayer perceptron in time series forecasting

4. Time series forecasting using deep learning

5. Time series classification and anomaly detection using deep learning

Getting Started

Install Anaconda Individual Edition

Download and install Anaconda.

Environment Setup

You can install the dependencies through any of the following ways:

1. Setup the virtual environment using conda by

conda env create -f environment.yml

2. Setup the virtual environment using virtualenv with Python version 3.8 by

pip install -r requirement.txt

The environment setup will take some time to download required modules.

GPU Setup (optional)

Follow the instructions below if you plan to use GPU setup.

1. Install CUDA and cuDNN Requirements:
   • CUDA 10.2
   • cuDNN 7.6

Step by step installation guides can be found here.

2. If you like to use different version of CUDA, please install appropriate cudatoolkit module by enter conda install cudatoolkit=CUDA_VERSION

conda install cudatoolkit=10.2

Usage

All examples are separated into [training] and [solution] folders.

All notebooks in training folder have few lines commented out so that they can be taught and demonstrated in the class. The solution folder contains the un-commented version for every line of codes.

Known Issues