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TSFM_Building

Pytorch implementation of [BuildSyS '24] Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?

Usage

The following code will run inference on each model and dataset:

python main.py --model YOUR_MODEL --real_data DATA

Here,

real_data \in {ecobee, electricity_uci, umass}

model \in {AutoARIMA,
              SeasonaARIMA,
              moment,
              chronos,
              TimeGPT,
              TimesFM,
              uni2ts
              ...}

Download dataset

Download electricity_uci dataset

Download the dataset from ElectricityLoadDiagrams20112014 Then put LD2011_2014.txt in the ./data/ folder.

Download ecobee dataset

Download the dataset Ecobee Then put combined_thermostat_data.csv in the ./data/ folder.

Download smart* dataset

The smart* (umass) dataset has been uploaded with the repo.

TS-foundation-model Setup

To reproduce the results, please first setup environments for each model:

Chronos installation

You can find it in this repo

Create an environment for Chronos

virtualenv chronos -p python3.10
source chronos/bin/activate

Install general packages

pip install -r requirements.txt

Install chronos

python3.10 -m pip install git+https://github.com/amazon-science/chronos-forecasting.git

Moment installation

You can find it in this repo

Create a environment for Moment. Please note that only python version >= 3.10 is supported.

virtualenv moment -p python3.10
source moment/bin/activate

Install general packages

pip install -r requirements.txt

Install moment

python3.10 -m pip install git+https://github.com/moment-timeseries-foundation-model/moment.git

Uni2ts installation

  1. Clone repository:
git clone https://github.com/SalesforceAIResearch/uni2ts.git
cd uni2ts
  1. Create virtual environment:
virtualenv uni2ts -p python3.10
source uni2ts/bin/activate
  1. Build from source:
pip install -e '.[notebook]'

Install general packages

pip install -r requirements.txt

TimeGPT installation

Install general packages

pip install -r requirements.txt

pip install nixtla>=0.5.1

need to get api key from nixtla

import pandas as pd
from nixtla import NixtlaClient


# 1. Instantiate the NixtlaClient
nixtla_client = NixtlaClient(api_key = 'YOUR API KEY HERE')

TimesFM installation

View the original repo. Follow the instruction in the repo to install the model.

[Update on 09/30/24] TimesFM can be installed via pip install timesfm

LagLlama installation

Create a new conda env

<!-- conda create -n LagLlama python=3.10
conda activate LagLlama -->
virtualenv LagLlama -p python3.10
source LagLlama/bin/activate

Install general packages

pip install -r requirements.txt

clone the repo

!git clone https://github.com/time-series-foundation-models/lag-llama/

Hyperlink your lag-llama folder to the current folder

ln -s <Your_lag-llama_folder> ./lag_src

Copy the model file and go to the lag-llama folder

cp lagllama_model.py ./lag_src
cd ./lag_src

install the requirements

!pip install -r requirements.txt --quiet # this could take some time # ignore the errors displayed by colab

download pretrained model weights from HuggingFace 🤗

!huggingface-cli download time-series-foundation-models/Lag-Llama lag-llama.ckpt --local-dir ./

copy the model file to lag-llama

Contact Information

If you have any questions or feedback, feel free to reach out: