By, ** Ahmed Nabil Atwa**, Data Scientist | Machine Learning | Deep learning Researcher

## Definition of Time Series

Time series is an ordered sequence of values of variables at equally spaced time intervals.

To understand time series, we shall start with understanding the difference between prediction and forecasting.

- Prediction is a definitive and specific statement that gives you a future perspective depending on a given dataset.
- For example, the share of this stock will increase in September 2021.

- Forecasting is a probabilistic statement, over a specific time scale.
- For example, the share of this stock will increase by 30% over the next couple of days.

So, from what we understand, we can see that time series depends on the **probability of the observations**. Time Series may typically be hourly, daily, weekly, monthly, quarterly and annual.

But, linear regression depends on **past values** to estimate the predicted values, whether it is a future, current or past.

## So, How to work on time series forecasting?

There’s a library called `sktime`

.

#### Anaconda

You can install it using `conda`

:

`conda install -c conda-forge sktime`

or with maximum dependencies,

`conda install -c conda-forge sktime-all-extras`

or

#### pip

Using pip, sktime releases are available as source packages and binary wheels. You can see all available wheels here.

`pip install sktime`

or, with maximum dependencies,

`pip install sktime[all_extras]`

### What’s sktime?

- sktime is a unified toolbox for machine learning with time series.
- Time series give rise to multiple learning tasks (e.g. forecasting and time series classification).
- The goal of sktime is to provide all the necessary tools to solve these tasks, including dedicated time series algorithms as well as tools for building, tuning and evaluating composite models.

Many of these tasks are related. An algorithm that can solve one of them can often be re-used to help solve another one, an idea called reduction. sktime’s unified interface allows to easily adapt an algorithm for one task to another.

### Forecasting with sktime

- In forecasting, past data is used to make temporal forward predictions of a time series. This is notably different from tabular prediction tasks supported by
`scikit-learn`

and similar libraries.

### Time series data and statistical dependence

- An intrinsic characteristic of time series is that observations are statistically dependent on past observations. So, they don’t naturally fit into the standard machine learning setting where we assume to have instances.
- In multivariate data, it is implausible to assume that the different univariate component time series are independent and identically distributed.

`sktime`

uses `pandas`

for representing time series:

`pd.Series`

for univariate time series and sequences.`pd.DataFrame`

for multivariate time series and sequenes.

The `Series.index`

and `DataFrame.index`

are used for representing the time series or sequence index. `sktime`

supports pandas integer, period and timestamp indices.

### Structure

For More details, you can check these notebooks and see more about the library and forecasting. In this Article, we will work on using Forecasting.

- Load dataset.
- Dat Preprocessing.
- Data Wrangling.
- Exploratory Data Analysis.

- Train Test Split
- Algorithm Setup.
- Fit Model.
- Predict Model.
- Evaluate Model.

## Import Libraries

In [58]:

importpandasaspdimportnumpyasnpfromsktime.datasetsimportload_airlinefromsktime.utils.plottingimportplot_seriesimportmatplotlib.pylabasplt%matplotlibinline

## 1. Load dataset

The dataset we have is **Box-Jenkins** airline dataset, which consists of the number of monthly totals of international airline passengers, from 1949 – 1960. Values are in thousands.In [7]:

series_airline=load_airline() series_airline

Out[7]:

Period 1949-01 112.0 1949-02 118.0 1949-03 132.0 1949-04 129.0 1949-05 121.0 ... 1960-08 606.0 1960-09 508.0 1960-10 461.0 1960-11 390.0 1960-12 432.0 Freq: M, Name: Number of airline passengers, Length: 144, dtype: float64

After loading the data – `sktime`

follows a workflow similar to the `sklearn`

workflow.

The steps in this workflow are as follows:

- Data Preprocessing.
- Build specification time points for which forecasts are requested.
- We can use
`numpy.array`

or the`ForecastingHorizon`

object.

- Split our data into train and test datasets.
- Building forecasting model and prepare its algorithm.
- Fitting the forecaster to the data, using the forecaster’s
`fit`

method. - Making a forecast; using the forecaster’s
`predict`

method. - Evaluate the forecast model.

## 2. Data Preprocessing

Display our data to start work on it.In [8]:

# Plotting for visualizationplot_series(series_airline) plt.show()

In [10]:

# Check the index of the seriesseries_airline.index

Out[10]:

PeriodIndex(['1949-01', '1949-02', '1949-03', '1949-04', '1949-05', '1949-06', '1949-07', '1949-08', '1949-09', '1949-10', ... '1960-03', '1960-04', '1960-05', '1960-06', '1960-07', '1960-08', '1960-09', '1960-10', '1960-11', '1960-12'], dtype='period[M]', name='Period', length=144, freq='M')

**NOTE:**

Generally, users are expected to use the in-built loading functionality of pandas and pandas-compatible packages to load data sets for forecasting, such as `read_csv`

or the `Series`

or `DataFrame`

constructors if data is available in another in-memory format, e.g., `numpy.array`

.

sktime forecasters may accept input in pandas-adjacent formats, but will produce outputs in, and attempt to coerce inputs to, `pandas`

formats.

NOTE: if your favourite format is not properly converted or coerced, kindly consider to contribute that functionality to `sktime`

.

Now, we need to specify the **forecasting horizon** and pass that to our forecasting algorithm

**numpy.array**In [12]:

fh=np.arange(1, 37) fh

Out[12]:

array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36])

This will ask for monthly predictions for the next three years, since the original series period is 1 month. In another example, to predict only the second and fifth month ahead, one could write:

importnumpyasnp fh=np.array([2, 5])# 2nd and 5th step ahead

**ForecastingHorizon**

The `ForecastingHorizon`

object takes absolute indices as input, but considers the input absolute or relative depending on the `is_relative`

flag.

`ForecastingHorizon`

will automatically assume a relative horizon if temporal difference types from `pandas`

are passed; if value types from `pandas`

are passed, it will assume an absolute horizon.

To define an absolute `ForecastingHorizon`

in our example:In [13]:

fromsktime.forecasting.baseimportForecastingHorizon

In [32]:

three_yrs_range=pd.PeriodIndex(pd.date_range('1961-01', periods=36, freq='M')) fh=ForecastingHorizon(three_yrs_range, is_relative=False) fh

Out[32]:

ForecastingHorizon(['1961-01', '1961-02', '1961-03', '1961-04', '1961-05', '1961-06', '1961-07', '1961-08', '1961-09', '1961-10', '1961-11', '1961-12', '1962-01', '1962-02', '1962-03', '1962-04', '1962-05', '1962-06', '1962-07', '1962-08', '1962-09', '1962-10', '1962-11', '1962-12', '1963-01', '1963-02', '1963-03', '1963-04', '1963-05', '1963-06', '1963-07', '1963-08', '1963-09', '1963-10', '1963-11', '1963-12'], dtype='period[M]', freq='M', is_relative=False)

`ForecastingHorizon`

-s can be converted from relative to absolute and back via the `to_relative`

and `to_absolute`

methods. Both of these conversions require a compatible `cutoff`

to be passed:In [20]:

cutoff=pd.Period("1960-12", freq='M') cutoff

Out[20]:

Period('1960-12', 'M')

In [29]:

fh.to_relative(cutoff)

Out[29]:

ForecastingHorizon([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36], dtype='int64', is_relative=True)

In [24]:

fh.to_absolute(cutoff)

Out[24]:

ForecastingHorizon(['1961-01-31', '1961-02-28', '1961-03-31', '1961-04-30', '1961-05-31', '1961-06-30', '1961-07-31', '1961-08-31', '1961-09-30', '1961-10-31', '1961-11-30', '1961-12-31', '1962-01-31', '1962-02-28', '1962-03-31', '1962-04-30', '1962-05-31', '1962-06-30', '1962-07-31', '1962-08-31', '1962-09-30', '1962-10-31', '1962-11-30', '1962-12-31', '1963-01-31', '1963-02-28', '1963-03-31', '1963-04-30', '1963-05-31', '1963-06-30', '1963-07-31', '1963-08-31', '1963-09-30', '1963-10-31', '1963-11-30', '1963-12-31'], dtype='datetime64[ns]', freq='M', is_relative=False)

## 3. Temporal Train and test batch

In [33]:

fromsktime.forecasting.model_selectionimporttemporal_train_test_split

In [128]:

y_train, y_test=temporal_train_test_split(series_airline, test_size=36)

We will try to forecast y_test from y_trainIn [38]:

# Plotting for illustrationplot_series(y_train, y_test, labels=['y_train', 'y_test']) plt.show() print(f"Y Train Shape: {y_train.shape[0]}\nY Test Shape: {y_test.shape[0]}")

Y Train Shape: 108 Y Test Shape: 36

#### Why can you use sklearn train_test_split instead of sktime temporal_train_test_split?

In [59]:

fromsklearn.model_selectionimporttrain_test_split

In [60]:

learn_y_train, learn_y_test=train_test_split(series_airline)

In [65]:

# Visulaize the dataplot_series(learn_y_train.sort_index(), learn_y_test.sort_index(), labels=['y_train', 'y_test']) plt.show()

This leads to **leakage**:

The data you are using to train a machine learning algorithm happens to have the information you’re trying to predict.

But `train_test_split(learn_y, shuffle=False)`

works, Which is what `temporal_train_test_split(y)`

does in `sktime`

.

That’s mean, the data you shuffled has lost the dependencies on its previous variables which connected with.

## 4. Algorithm setup

In [39]:

fromsktime.forecasting.naiveimportNaiveForecaster

In [41]:

# We can simply take the indices from `y_test` where they already are storedfh=ForecastingHorizon(y_test.index, is_relative=False)# apply the algorithmforecast_model=NaiveForecaster(strategy='last', sp=12)

## 5. Fitting Model

In [42]:

forecast_model.fit(y_train)

Out[42]:

NaiveForecaster(sp=12)

## 6. Predict Model

In [129]:

y_pred=forecast_model.predict(fh)

#### Plotting for illustration

In [45]:

plot_series(y_train, y_test, y_pred, labels=['y_train', 'y_test', 'y_pred']) plt.show()

## 7. Evaluate Model

The next step is to specify a forecasting metric. These are functions that return a number when input with prediction and actual series. They are different from `sklearn`

metrics in that they accept series with indices rather than `np.array`

s. Forecasting metrics can be invoked in two ways:

- using the lean
**function interface**, e.g.,`mean_absolute_percentage_error`

which is a python function`(y_true : pd.Series, y_pred : pd.Series) -> float`

- using the composable
**class**interface, e.g.,`MeanAbsolutePercentageError`

, which is a python class, callable with the same signature (*compu

`mean_absolute_percentage_error`

In [46]:

fromsktime.performance_metrics.forecastingimportmean_absolute_percentage_error

In [47]:

mean_absolute_percentage_error(y_test, y_pred)

Out[47]:

0.145427686270316

`MeanAbsolutePercentageError`

In [49]:

fromsktime.performance_metrics.forecastingimportMeanAbsolutePercentageError

In [50]:

mape=MeanAbsolutePercentageError(symmetric=False) mape(y_test, y_pred)

Out[50]:

0.13189432350948402

`mean_squared_percentage_error`

In [66]:

fromsktime.performance_metrics.forecastingimportmean_squared_percentage_error

In [67]:

mean_squared_percentage_error(y_test, y_pred)

Out[67]:

0.030181651132460994

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- ISmile Technologies provides advanced machine learning and predictive modeling techniques to analyze and forecast time series data.
- With its expertise in statistical modeling and time series analysis, ISmile can provide more accurate and reliable forecasts than simple linear regression.
- ISmile’s forecasting models take into account various factors such as seasonality, trends, and cyclic patterns, which are often overlooked in linear regression.
- By using machine learning algorithms and advanced statistical models, ISmile can quickly and efficiently process large amounts of data, resulting in more timely and accurate forecasts.

- In addition to forecasting, ISmile can provide valuable insights and recommendations for businesses to improve their decision-making and optimize their operations.

ISmile Technologies can help businesses gain a competitive advantage by providing more accurate and efficient time series forecasting than traditional linear regression methods. By leveraging advanced machine learning algorithms and statistical models, ISmile can provide valuable insights and recommendations to optimize business operations and drive growth.

## Summary

- To summarize that, we can say, if your model starts working on observations that depend on time scale – it will be more efficient to use time series forecasting – this will help you to have precise forecasting according to the selected time.
- For instance, if you’re looking for getting the price of a share over the next day, you will need to use time-series to forecast the share’s price.

- But, If your observations contain only continuous data and you’re looking to predict estimate values, whether it is future, current, or past values.
- For instance, if you’re looking for getting the prices of the shares in August, you need to give the model the last three months of the observation to analyze it and give the prediction of these shares.