# Predict Number of Taxi Passengers with Chronos Forecaster

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![](../../../../image/colab_logo_32px.png)[Run in Google Colab][chronos_nyc_taxi_tsdataset_forecaster_colab] &nbsp;![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub][chronos_nyc_taxi_tsdataset_forecaster]

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**In this guide we will demonstrate how to use _Chronos TSDataset_ and _Chronos Forecaster_ for time seires processing and forecasting in 4 simple steps.**

### **Step 0: Prepare Environment**

We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../Overview/chronos.html#install) for more details.

```bash
conda create -n my_env python=3.7 # "my_env" is conda environment name, you can use any name you like.
conda activate my_env
pip install bigdl-chronos[all]
```

### Step 1: Data transformation and feature engineering using Chronos TSDataset

[TSDataset](../Overview/data_processing_feature_engineering.html) is our abstract of time series dataset for data transformation and feature engineering. Here we use it to preprocess the data.

Initialize train, valid and test tsdataset from raw pandas dataframe.

```python
from bigdl.chronos.data import TSDataset
from sklearn.preprocessing import StandardScaler

tsdata_train, tsdata_valid, tsdata_test = TSDataset.from_pandas(df, dt_col="timestamp", target_col="value",
                                                                with_split=True, val_ratio=0.1, test_ratio=0.1)
```
Preprocess the datasets. Here we perform:

- deduplicate: remove those identical data records
- impute: fill the missing values
- gen_dt_feature: generate feature from datetime (e.g. month, day...)
- scale: scale each feature to standard distribution.
- roll: sample the data with sliding window.
- For forecasting task, we will look back 3 hours' historical data (6 records) and predict the value of next 30 miniutes (1 records).

We perform the same transformation processes on train, valid and test set.

```python
lookback, horizon = 6, 1

scaler = StandardScaler()
for tsdata in [tsdata_train, tsdata_valid, tsdata_test]:
    tsdata.deduplicate().impute().gen_dt_feature()\
          .scale(scaler, fit=(tsdata is tsdata_train))\
          .roll(lookback=lookback, horizon=horizon)
```

### Step 2: Time series forecasting using Chronos Forecaster

After preprocessing the datasets. We can use [Chronos Forecaster](../Overview/forecasting.html#use-standalone-forecaster-pipeline) to handle the forecasting tasks.

Transform TSDataset to sampled numpy ndarray and feed them to forecaster.

```python
x, y = tsdata_train.to_numpy() 
x_val, y_val = tsdata_valid.to_numpy() 
# x.shape = (num of sample, lookback, num of input feature)
# y.shape = (num of sample, horizon, num of output feature)

forecaster = TCNForecaster(past_seq_len=lookback,  # number of steps to look back
                           future_seq_len=horizon,  # number of steps to predict
                           input_feature_num=x.shape[-1],  # number of feature to use
                           output_feature_num=y.shape[-1])  # number of feature to predict
res = forecaster.fit(data=(x, y), epochs=3)
```

### Step 3: Further deployment with fitted forecaster

Use fitted forecaster to predict test data

```python
x_test, y_test = tsdata_test.to_numpy()
pred = forecaster.predict(x_test)
pred_unscale, groundtruth_unscale = tsdata_test.unscale_numpy(pred), tsdata_test.unscale_numpy(y_test)
```

Save & restore the forecaster.

```python
forecaster.save("nyc_taxi.fxt")
forecaster.restore("nyc_taxi.fxt")
```

[chronos_nyc_taxi_tsdataset_forecaster_colab]:<https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>
[chronos_nyc_taxi_tsdataset_forecaster]:<https://github.com/intel-analytics/BigDL/blob/main/python/chronos/colab-notebook/chronos_nyc_taxi_tsdataset_forecaster.ipynb>