In this lesson, you will discover a specific way to build models with [ARIMA: *A*uto*R*egressive *I*ntegrated *M*oving *A*verage](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average). ARIMA models are particularly suited to fit data that shows [non-stationarity](https://wikipedia.org/wiki/Stationary_process).
🎓 **Stationarity**
## General concepts
To be able to work with ARIMA, there's some concepts you need to know about:
From a statistical context, stationarity refers to data whose distribution does not change when shifted in time. Non-stationary data, then, shows fluctuations due to trends that must be transformed to be analyzed. Seasonality, for example, can introduce fluctuations in data and can be eliminated by a process of 'seasonal-differencing'.
- 🎓 **Stationarity**. From a statistical context, stationarity refers to data whose distribution does not change when shifted in time. Non-stationary data, then, shows fluctuations due to trends that must be transformed to be analyzed. Seasonality, for example, can introduce fluctuations in data and can be eliminated by a process of 'seasonal-differencing'.
- 🎓 **[Differencing](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average#Differencing)**. Differencing data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series." [Paper by Shixiong et al](https://arxiv.org/abs/1904.07632)
Differencing data, again from a statistical context, refers to the process of transforming non-stationary data to make it stationary by removing its non-constant trend. "Differencing removes the changes in the level of a time series, eliminating trend and seasonality and consequently stabilizing the mean of the time series." [Paper by Shixiong et al](https://arxiv.org/abs/1904.07632)
## ARIMA in the context of time series
Let's unpack the parts of ARIMA to better understand how it helps us model time series and help us make predictions against it.
## AR - for AutoRegressive
Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average)
## I - for Integrated
- **AR - for AutoRegressive**. Autoregressive models, as the name implies, look 'back' in time to analyze previous values in your data and make assumptions about them. These previous values are called 'lags'. An example would be data that shows monthly sales of pencils. Each month's sales total would be considered an 'evolving variable' in the dataset. This model is built as the "evolving variable of interest is regressed on its own lagged (i.e., prior) values." [wikipedia](https://wikipedia.org/wiki/Autoregressive_integrated_moving_average)
As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity.
## MA - for Moving Average
- **I - for Integrated**. As opposed to the similar 'ARMA' models, the 'I' in ARIMA refers to its *[integrated](https://wikipedia.org/wiki/Order_of_integration)* aspect. The data is 'integrated' when differencing steps are applied so as to eliminate non-stationarity.
The [moving-average](https://wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags.
- **MA - for Moving Average**. The [moving-average](https://wikipedia.org/wiki/Moving-average_model) aspect of this model refers to the output variable that is determined by observing the current and past values of lags.
Bottom line: ARIMA is used to make a model fit the special form of time series data as closely as possible.
### Exercise: build an ARIMA model
Open the `/working` folder in this lesson and find the _notebook.ipynb_ file. Run the notebook to load the `statsmodels` Python library; you will need this for ARIMA models.
## Exercise - build an ARIMA model
Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.
1. Run the notebook to load the `statsmodels` Python library; you will need this for ARIMA models.
1. Load necessary libraries
Now, load up several more libraries useful for plotting data:
```python
import os
import warnings
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
import math
from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.statespace.sarimax import SARIMAX
Now your data is loaded, so you can separate it into train and test sets. You'll train your model on the train set. As usual, after the model has finished training, you'll evaluate its accuracy using the test set. You need to ensure that the test set covers a later period in time from the training set to ensure that the model does not gain information from future time periods.
Allocate a two-month period from September 1 to October 31, 2014 to the training set. The test set will include the two-month period of November 1 to December 31, 2014.
1. Allocate a two-month period from September 1 to October 31, 2014 to the training set. The test set will include the two-month period of November 1 to December 31, 2014:
```python
train_start_dt = '2014-11-01 00:00:00'
test_start_dt = '2014-12-30 00:00:00'
```
```python
train_start_dt = '2014-11-01 00:00:00'
test_start_dt = '2014-12-30 00:00:00'
```
Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days. You can visualize the differences:
Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days.
Therefore, using a relatively small window of time for training the data should be sufficient.
> Note: Since the function we use to fit the ARIMA model uses in-sample validation during fitting, we will omit validation data.
Therefore, using a relatively small window of time for training the data should be sufficient.
5. Prepare the data for training
> Note: Since the function we use to fit the ARIMA model uses in-sample validation during fitting, we will omit validation data.
Now, you need to prepare the data for training by performing two tasks:
### Prepare the data for training
- Filter the original dataset to include only the aforementioned time periods per set and only including the needed column 'load' plus the date:
Now, you need to prepare the data for training by performing filtering and scaling of your data. Filter your dataset to only include the time periods and columns you need, and scaling to ensure the data is projected in the interval 0,1.
Now that you have calibrated the scaled data, you can scale the test data:
> The scaled data
```python
test['load'] = scaler.transform(test)
test.head()
```
1. Now that you have calibrated the scaled data, you can scale the test data:
7. Implement ARIMA
```python
test['load'] = scaler.transform(test)
test.head()
```
### Implement ARIMA
It's time to implement ARIMA! You'll now use the `statsmodels` library that you installed earlier.
Now you need to follow several steps
1. Define the model by calling `SARIMAX()` and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters.
2. The model is prepared on the training data by calling the fit() function.
3. Predictions can be made by calling the `forecast()` function and specifying the number of steps (the `horizon`) to forecast
1. Define the model by calling `SARIMAX()` and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters.
2. Prepare the model for the training data by calling the fit() function.
3. Make predictions calling the `forecast()` function and specifying the number of steps (the `horizon`) to forecast.
> 🎓 What are all these parameters for? In an ARIMA model there are 3 parameters that are used to help model the major aspects of a time series: seasonality, trend, and noise. These parameters are:
@ -174,31 +181,33 @@ Now you need to follow several steps
> Note: If your data has a seasonal aspect - which this one does - , we use a seasonal ARIMA model (SARIMA). In that case you need to use another set of parameters: `P`, `D`, and `Q` which describe the same associations as `p`, `d`, and `q`, but correspond to the seasonal components of the model.
Start by setting your preferred horizon value. Let's try 3 hours:
```python
# Specify the number of steps to forecast ahead
HORIZON = 3
print('Forecasting horizon:', HORIZON, 'hours')
```
1. Start by setting your preferred horizon value. Let's try 3 hours:
Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html), but for now try some manual selections to find a good model.
```python
# Specify the number of steps to forecast ahead
HORIZON = 3
print('Forecasting horizon:', HORIZON, 'hours')
```
```python
order = (4, 1, 0)
seasonal_order = (1, 1, 0, 24)
Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html),
model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
results = model.fit()
1. For now try some manual selections to find a good model.
print(results.summary())
```
```python
order = (4, 1, 0)
seasonal_order = (1, 1, 0, 24)
model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
results = model.fit()
print(results.summary())
```
A table of results is printed.
A table of results is printed.
You've built your first model! Now we need to find a way to evaluate it.
8. Evaluate your model
### Evaluate your model
To evaluate your model, you can perform the so-called `walk forward` validation. In practice, time series models are re-trained each time a new data becomes available. This allows the model to make the best forecast at each time step.
@ -210,95 +219,97 @@ This process provides a more robust estimation of how the model will perform in
Walk-forward validation is the gold standard of time series model evaluation and is recommended for your own projects.
First, create a test data point for each HORIZON step.
1. First, create a test data point for each HORIZON step.
Observe the hourly data's prediction, compared to the actual load. How accurate is this?
### Check model accuracy
Check the accuracy of your model by testing its mean absolute percentage error (MAPE) over all the predictions.
> **🧮 Show me the math**
>
@ -306,73 +317,78 @@ Check the accuracy of your model by testing its mean absolute percentage error (
>
> [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actual<sub>t</sub> and predicted<sub>t</sub> is divided by the actual<sub>t</sub>. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))
ax.legend(loc='best')
plt.xlabel('timestamp', fontsize=12)
plt.ylabel('load', fontsize=12)
plt.show()
```
🏆 A very nice plot, showing a model with good accuracy. Well done!
---
## 🚀Challenge
Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models.
softchris
3 years ago