in the coming sectionswe will apply our understanding of time seriespreprocessing techniquesand so on to solve stock and commodity price forecasting problems using different forecasting methods forecasting gold price goldthe yellow shiny metalhas been the fancy of mankind since ages from making jewelry to being used as an investmentgold covers huge spectrum of use cases goldlike other metalsis also traded on the commodities indexes across the world for better understanding time series in real-world scenariowe will work with gold prices collected historically and predict its future value let' begin by first formally stating the problem statement problem statement metals such as gold have been traded for years across the world prices of gold are determined and used for trading the metal on commodity exchanges on daily basis using variety of factors using this daily price-level information onlyour task is to predict future price of gold dataset for any problemfirst and foremost is the data stock and commodity exchanges do wonderful job of storing and sharing daily level pricing data for the purpose of this use casewe will utilize gold pricing from quandl quandl is platform for financialeconomicand alternative datasets you can refer to the jupyter notebook notebook_gold_forecast_arima ipynb for the necessary code snippets and examples to access publicly shared datasets on quandlwe can use the pandas-datareader library as well as quandl (library from quandl itself for this use casewe will depend upon quandl kindly install the same using pip or conda the following snippet shows quick one-liner to get your hands on gold pricing information since in [ ]import quandl gold_df quandl get("bundesbank/bbk _wt "end_date=""the get(function takes the stock/commodity identifier as first parameter followed by the date until which we need the data note that not all datasets are publicfor some of themapi access must be obtained traditional approaches time series analysis and forecasting have been long studied in detail there are matured and extensive set of modeling techniques available for the same out of the manythe following are few most commonly used and explored techniques simple moving average and exponential smoothing based forecasting holt'sholt-winter' exponential smoothing based forecasting box-jenkins methodology (armaarimas-arimaetc