nixtla_poc.qmd

---
title: "Nixtla Demo"
format:
  html:
    code-fold: false
jupyter: python3
---

## Load Libraries

```{python}
import pandas as pd
import numpy as np
import datetime

import statsforecast as sf
import statsforecast.models as sfm
from utilsforecast.plotting import plot_series
```


## Load Data

```{python}
url = "https://raw.githubusercontent.com/LinkedInLearning/data-pipeline-automation-with-github-actions-4503382/main/csv/ciso_grid_py.csv"

d = pd.read_csv(url)

d.head()

```


```{python}
d["ds"] = pd.to_datetime(d["period"])
d = d[["ds", "subba", "value"]]
start = datetime.datetime(2022, 8, 1, 0, 0, 0)
end = datetime.datetime(2024, 8, 20, 23, 0, 0)


subba = d["subba"].dropna().unique()
ts = None

for i in range(len(subba)):
    s = subba[i]
    id = i + 1
    ts_temp = pd.DataFrame(np.arange(start = start, stop = end + datetime.timedelta(hours = 1), step = datetime.timedelta(hours = 1)).astype(datetime.datetime), columns=["ds"])
    ts_temp["unique_id"] = id
    ts_temp["subba"] = s
    ts_temp = ts_temp.merge(d, on = ["ds", "subba"], how = "left")
    ts_temp = ts_temp.sort_values("ds")
    if ts_temp["value"].isnull().any():
        r = ts_temp[ts_temp["value"].isnull()]
        for n in r.index:
            ts_temp.at[n, "value"] = (ts_temp.at[n - 1, "value"] + ts_temp.at[n - 24, "value"] + ts_temp.at[n - 24 * 7, "value"]) / 3 
    ts_temp = ts_temp.rename(columns = {"value": "y"})

    if ts is None:
        ts = ts_temp
    else:
        ts =  pd.concat([ts, ts_temp])

ts = ts[["ds", "unique_id", "y"]]
ts.head()
```



```{python}
plot_series(ts, engine = "plotly")
```


## Training Models

```{python}
test_length = 72

train_end = end  - datetime.timedelta(hours = test_length)


train = ts[ts["ds"] <= train_end]
test = ts[ts["ds"] > train_end]

plot_series(test, engine = "plotly")
```


```{python}
auto_arima = sfm.AutoARIMA()

s_naive = sfm.SeasonalNaive(season_length=24)

theta =  sfm.DynamicOptimizedTheta(season_length= 24)

mstl1 = sfm.MSTL(
    season_length=[24, 24 * 7], 
    trend_forecaster=sfm.AutoARIMA(),
    alias="MSTL_ARIMA_trend"
)

mstl2 = sfm.MSTL(
    season_length=[24, 24 * 7], 
    trend_forecaster= sfm.HoltWinters(),
    alias="MSTL_HW_trend" 
)

stats_models = [auto_arima, s_naive, theta, mstl1, mstl2]



md = sf.StatsForecast( 
    models=stats_models,
    freq="h", 
    fallback_model = sfm.AutoARIMA(),
    n_jobs= -1,
)

```


```{python}
forecast_stats = md.forecast(df=train, h=72, level=[95])

print(forecast_stats.head())

md.plot(test, forecast_stats,engine = "plotly", level=[95])
```