https://github.com/dusty-nv/pytorch-timeseries
https://github.com/dusty-nv/pytorch-timeseries
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/dusty-nv/pytorch-timeseries
- Owner: dusty-nv
- Created: 2021-06-14T16:33:49.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-06-01T14:37:40.000Z (about 2 years ago)
- Last Synced: 2025-03-25T17:48:24.485Z (2 months ago)
- Language: Python
- Size: 3.68 MB
- Stars: 9
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# pytorch-timeseries
Time-series forecasting and prediction on tabular data using PyTorch and NVIDIA Jetson.
#### Starting the Container
``` bash
$ git clone https://github.com/dusty-nv/pytorch-timeseries
$ cd pytorch-timeseries
$ docker/run.sh -c nvcr.io/nvidia/l4t-ml:rXX.X-py3 # select l4t-ml based on your JetPack-L4T version
$ cd pytorch-timeseries
```
When you do the `docker/run.sh` command, pick one of the [l4t-ml](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-ml) container tags for the version of JetPack-L4T that you are running. For example, if you're on JetPack 4.6 / L4T R32.7, use `nvcr.io/nvidia/l4t-ml:r32.7.1-py3`
#### Weather Forecasting
In this [hourly weather dataset](https://www.kaggle.com/selfishgene/historical-hourly-weather-data), we are forecasting the future weather of a city based on it's past weather. The following data is provided on 1-hour intervals (over a timespan of 40,000 hours):
```
temperature
humidity
pressure
wind_direction
wind_speed
```
Let's start by forecasting just the temperature:
``` bash
$ python3 train.py --data data/weather.csv --inputs temperature --outputs temperature --horizon 1
train RMSE: [1.7638184641368702]
train R2: [0.9768890819461414]
val RMSE: [1.788160646675811]
val R2: [0.9815134518452414]
```
Next we can incorporate multiple inputs/outputs and simultaneously predict the temperature, humidity, and barometric pressure:
``` bash
$ python3 train.py --data data/weather.csv --inputs temperature,humidity,pressure --outputs temperature,humidity,pressure --horizon 1
train RMSE: [1.8732083404885846, 7.086468834684506, 12.398559674547002]
train R2: [0.9735385787621987, 0.7405181652792221, 0.4753518787407489]
val RMSE: [1.8272543024543522, 10.621018369410514, 3.4025864627892943]
val R2: [0.9821397234156162, 0.5664843260674557, 0.8365918355499764]
```
#### Solar Power Prediction
This [dataset](https://www.kaggle.com/anikannal/solar-power-generation-data) contains solar panel power generation data along with environmental conditions from a solar power plant. The goal is to predict generated power given sthe ambient temperature and amount of sunlight. In this example, we are predicting 2 outputs (AC and DC power)
``` bash
$ python3 train.py --data data/solar_power.csv --inputs AMBIENT_TEMPERATURE,IRRADIATION --outputs DC_POWER,AC_POWER
train RMSE: [6912.877912624019, 660.7508599953275]
train R2: [0.9940444439752174, 0.9942998898150646]
val RMSE: [4932.04556598682, 482.82950183757634]
val R2: [0.9963377448275983, 0.9963286618121212]
```
#### Space Shuttle Classification
This classification dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Statlog+%28Shuttle%29) contains the values from 9 sensors and has 7 state classes:
```
class distribution:
[0] 'Rad Flow' - 45586 samples
[1] 'Fpv Close' - 50 samples
[2] 'Fpv Open' - 171 sampless
[3] 'High' - 8903 samples
[4] 'Bypass' - 3267 samples
[5] 'Bpv Close' - 10 samples
[6] 'Bpv Open' - 13 samples
total: 58000 samples
```
The goal is to predict the state of the system from the current sensor data. Given the unbalanced distribution of data between the classes, this example is akin to anomoly detection.
``` bash
$ python3 train.py --data data/shuttle.csv --inputs 0,1,2,3,4,5,6,7,8 --outputs class --classification --epochs 100
train accuracy: [0.9995689655172414]
train precision: [0.9995585721164452]
train recall: [0.9995689655172414]
train F1: [0.9995573360381868]
val accuracy: [0.9987068965517242]
val precision: [0.9987528787795457]
val recall: [0.9987068965517242]
val F1: [0.9986359711781727]
```